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BMAI

Biomedical Artificial
Intelligence Research Unit

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Publications

Impact Factors (IF) of Journals by Clarivate Analytics (ex Thomson/ISI) (2022)

  • IEEE Transactions on Pattern Analysis and Machine Intelligence

    23.6

  • IEEE Transactions on Image Processing

    10.6

  • Pattern Recognition

    8.0

  • Knowledge-Based Systems

    8.8

  • IEEE Transactions on Signal Processing

    5.4

  • IEEE Transactions on Medical Imaging

    10.6

  • IEEE Transactions on Biomedical Engineering

    4.6

  • IEEE Journal of Biomedical and Health Informatics

    7.7

  • European Journal of Nuclear Medicine and Molecular Imaging

    9.1

  • Radiology

    19.7

  • Liver Transplantation

    4.6

  • Journal of Magnetic Resonance Imaging

    4.4

  • Journal of Neural Engineering

    4.0

  • PLoS ONE

    3.7

  • Physics in Medicine and Biology

    3.5

  • Medical Physics

    3.8

  • Computer Vision and Image Understanding

    4.5

  • American Journal of Roentgenology

    5.0

  • Quantitative Imaging in Medicine and Surgery

    2.8

Citations by Google Scholar (as of 07/2023)

All Since 2018
Citations 14937 5818
h-index 59 38
i10-index 173 100
Google Scholar

Original Peer-Reviewed Papers in Journals

ID

Title

  • 1

    Pavarut S., Preedanan W., Kumazawa I., Suzuki K., Kobayashi M., Tanaka H., Ishioka J., Matsuoka Y., and Fujii Y.: Improving Kidney Tumor Classification With Multi-Modal Medical Images Recovered Partially by Conditional CycleGAN. IEEE Access 11: 146250 – 146261, 2023.

  • 2

    Rahmaniar W., Suzuki K., and Lin T.L.: Auto-CA: Automated Cobb Angle Measurement Based on Vertebrae Detection for Assessment of Spinal Curvature Deformity. IEEE Transactions on Biomedical Engineering: 1-10, 2023.

  • 3

    Shishido T. Ono Y., Kumazawa I. Iwai I., and Suzuki K.: Artificial intelligence model substantially improves stratum corneum moisture content prediction from visible-light skin images and skin feature factors. Skin Research and Technology 29(8): e13414, 2023.

  • 4

    Preedanan W., Suzuki K., Kondo T., Kobayashi M., Tanaka H., Ishioka J., Matsuoka Y., Fujii Y., and Kumazawa I.: Urinary Stones Segmentation in Abdominal X-Ray Images Using Cascaded U-Net Pipeline with Stone-Embedding Augmentation and Lesion-Size Reweighting Approach. IEEE Access 11: 25702-25712, 2023.

  • 5

    Hadjiiski L., Cha K., Chan H-P., Drukker K., Morra L., Nappi J. J. Sahiner B., Yoshida H., Chen Q., Deserno T. M., Greenspan H., Huisman H., Huo Z., Mazurchuk R., Petrick N., Regge D., Samala R., Summers R. M., Suzuki K., Tourassi G., Vergara D., and Armato III S. G.: AAPM task group report 273: Recommendations on best practices for AI and machine learning for computer-aided diagnosis in medical imaging, Medical Physics 50: 1-24, 2022.

  • 6

    Preedanan W., Suzuki K., Kondo T., Kobayashi M., Tanaka H., Ishioka J., Matsuoka Y., Fujii Y., and Kumazawa I.: Improvement of Urinary Stone Segmentation Using GAN-Based Urinary Stones Inpainting Augmentation. IEEE Access 10: 115131-115142, 2022.

  • 7

    Kobayashi M., Ishioka J., Matsuoka Y., Fukuda Y., Kohno Y., Kawano K., Morimoto S., Muta R., Fujiwara M., Kawamura N., Okuno T., Yoshida S., Yokoyama M., Suda R., Saiki R., Suzuki K., Kumazawa I., and Fujii Y.: Computer-aided diagnosis with a convolutional neural network algorithm for automated detection of urinary tract stones on plain X-ray. BMC Urology 21: 102, 2021.

  • 8

    Martínez-García M., Zhang Y., Suzuki K., and Zhang Y.: Deep Recurrent Entropy Adaptive Model for System Reliability Monitoring. IEEE Transactions on Industrial Informatics 17(2): 839-848, 2020.

  • 9

    Zarshenas A., Liu J., Forti P., and Suzuki K.: Separation of Bones from Soft Tissue in Chest Radiographs: Anatomy-specific Orientation-frequency-specific Deep Neural Network Convolution. Medical Physics 46(5): 2232-2242, 2019.

  • 10

    Shi Z., Hao H., Zhao M., Feng Y., He L., Wang Y., and Suzuki K.: A deep CNN based transfer learning method for false positive reduction. Multimedia Tools and Applications 78(1): 1017-1033, 2019.

  • 11

    He L., Zhao X., Yao B., Yang Y., Chao Y., Shi Z., and Suzuki K.: A Combinational Algorithm for Connected-Component Labeling and Euler Number Computing. Journal of Real-Time Image Processing 13 (4), 703-712, 2017.

  • 12

    Suzuki K.: Machine Learning in Medical Imaging Before and After Introduction of Deep Learning. Journal of Medical Imaging and Information Science 34 (2): 14-24, 2017. (Invited, peer-reviewed)

  • 13

    Suzuki K.: Overview of Deep Learning in Medical Imaging. Radiological Physics and Technology 10 (3): 257-273, 2017. (Invited, peer-reviewed)

  • 14

    Suzuki K.: Survey of Deep Learning Applications to Medical Image Analysis. Medical Imaging Technology 35 (4): 212-226, 2017. (Invited, peer-reviewed)

  • 15

    Chen Y., Chan A.B., Lin Z., Suzuki K., and Guoping Wanga: Efficient Tree-structured SfM by RANSAC Generalized Procrustes Analysis. Computer Vision and Image Understanding 157: 179-189, 2017.

  • 16

    Tajbakhsh N. and Suzuki K.: Comparing two classes of end-to-end machine-learning models in lung nodule detection and classification: MTANNs vs. CNNs Pattern Recognition 63: 476–486, 2017.

  • 17

    Huynh T. H., Ngoc T L., Bao T P., Aytek O., and Suzuki K.: Fully automated MR Liver Volumetry using Watershed Segmentation Coupled with Active Contouring. International Journal of Computer Assisted Radiology and Surgery 12 (2): 235–243, 2017.

  • 18

    Zarshenas A. and Suzuki K.: Binary Coordinate Ascent: An efficient optimization technique for feature subset selection for machine learning, Knowledge-Based Systems: 110: 191-201, 2016.

  • 19

    Shi Z., Ma J., Zhao M., Liu Y., Feng Y., Zhang M., He L., and Suzuki K.: Many is Better Than One: An Integration of Multiple Simple Strategies for Accurate Lung Segmentation in CT Images. BioMed Research International 2016: Article ID 1480423, 13 pages, 2016.

  • 20

    Sihai Y., Xu J., Suzuki K.: Density Index: Extension of Shape Index in Describing Local Intensity Variations in a 3D Image, Journal of Computer-Aided Design & Computer Graphics 28 (7): 1152-1159, 2016.

  • 21

    Chen S., Zhong S., Yao L., Yanfeng S., Suzuki K.: Enhancement of chest radiographs obtained in the intensive care unit through bone suppression and consistent processing, Physics in Medicine and Biology 61: 2283-2301, 2016.

  • 22

    Epstein M. L., Obara P. R., Chen Yi., Liu J., Zarshenas A., Makkinejad N., Dachman A. H., and Suzuki K.: Quantitative radiology: Automated measurement of polyp volume in computed tomography colonography using Hessian matrix-based shape extraction and volume growing. Quantitative Imaging in Medicine and Surgery 5: 673-684, 2015.

  • 23

    Dai P., Luo H., Sheng H., Zhao Y., Li L., Wu J., Zhao Y., Suzuki K.: A New Approach to Segment Both Main and Peripheral Retinal Vessels Based on Gray-voting and Gaussian Mixture Model, PLoS ONE 10(6): e0127748, 2015.

  • 24

    Shi Z., Ma J., Feng L., He L., and, Suzuki K.: Evaluation of MTANNs for eliminating false-positive with different computer aided pulmonary nodules detection software, Pakistan Journal of Pharmaceutical Sciences 28 (6): 2311-2316, 2015.

  • 25

    Shi Z., Xu B., Zhao M., Zhao J., Wang Y., Liu Y., Zhang M. He L., and Suzuki K.: A joint ROI extraction filter for computer aided lung nodule detection. Bio-Medical Materials and Engineering 26: 1491-1499, 2015.

  • 26

    Shi Z., Si C., Zhao M., He L., Zhang M., and Suzuki K.: An Automatic Method for Lung Segmentation in Thin Slice Computed Tomography Based on Random Walks. Journal of Medical Imaging and Health Informatics 5: 303-308, 2015.

  • 27

    Shi Z., Si C., Feng Y., He L., and, Suzuki K.: A new method based on MTANNs for cutting down false-positives: An evaluation on different versions of commercial pulmonary nodule detection CAD software, Bio-Medical Materials and Engineering 24: 2839–2846, 2014.

  • 28

    Wáng Y., Loffroy R., Arora R., Suzuki K., Chang-Hee Lee, Hsiao-Wen Chung, Edwin H.G. Oei, Gavin P Winston, Chin K. Ng: Relative income of clinical faculty members vs. science faculty members in university settings-a short survey of France, Hong Kong, India, Japan, South Korea, The Netherlands, Taiwan, UK, and USA, Quantitative Imaging in Medicine and Surgery 24(6): 500–501, 2014.

  • 29

    Wang Y., Gong J., Suzuki K., and Morcos S.K.: Evidence based imaging strategies for solitary pulmonary nodule.Journal of Thoracic Disease 6(7): 872-887, 2014.

  • 30

    Xu J., and Suzuki K.: Max-AUC Feature Selection in Computer-aided Detection of Polyps in CT Colonography. IEEE Journal of Biomedical and Health Informatics 18: 585-593, 2014 (Selected as a featured article on the cover page of the issue).

  • 31

    Chen S. and Suzuki K.: Separation of Bones from Chest Radiographs by Means of Anatomically Specific Multiple Massive-Training ANNs Combined with Total Variation Minimization Smoothing. IEEE Transactions on Medical Imaging 33: 246-257, 2014.

  • 32

    He L., Chao Y., and Suzuki K.: Configuration-Transition-Based Connected-Component Labeling. IEEE Transactions on Image Processing 23: 943-951, 2014.

  • 33

    Huynh T. H., Karademir I., Oto A., and Suzuki K.: Computerized Liver Volumetry on MRI by Using 3D Geodesic Active Contour Segmentation. American Journal of Roentgenology 202: 152-159, 2014.

  • 34

    Suzuki K.: Machine Learning in Computer-aided Diagnosis of the Thorax and Colon in CT: A Survey. IEICE Transactions on Information & Systems E96-D: 772-783, 2013. (Invited, peer-reviewed)(Awarded 2014 Best Paper Award from IEICE)

  • 35

    He L., Chao Y., and Suzuki K.: An Algorithm for Connected-Component Labeling, Hole Labeling and Euler Number Computing. Journal of Computer Science and Technology 28(3): 468-478, 2013.

  • 36

    Shi Z., Zhao M., He L., Wang Y., Zhang M., and Suzuki K.: A Computer Aided Pulmonary Nodule Detection System Using Multiple Massive Training SVMs. Applied Mathematics & Information Sciences 7: 1165-1172, 2013.

  • 37

    Chen S. and Suzuki K.: Computerized Detection of Lung Nodules by Means of “Virtual Dual-Energy” Radiography. IEEE Transactions on Biomedical Engineering 60: 369-378, 2013.

  • 38

    El-Baz A., Beache G. M., Gimel’farb G., Suzuki K., Okada K., Elnakib A., Soliman A., and Abdollahi B.: Computer aided diagnosis systems for lung cancer: Challenges and methodologies. International Journal of Biomedical Imaging 2013: Article ID 942353, 46 pages, 2013.

  • 39

    Shi Z., Zhao M., Wang Y., He L., Suzuki K., Jin C., and Zhang M.: Hessian-LoG: A Novel Dot Enhancement Filter. ICIC Express Letters 6: 1987-1992, 2012.

  • 40

    Shi, Z., Li, L., Suzuki, K., Wang, Y., He, L., Jin, C., Zhang, M.: A New Computer Aided Detection System for Pulmonary Nodule Detection in Chest Radiography. Advanced Science Letters 11: 536-541, 2012.

  • 41

    Yu Q., He L., Nakamura T., Chao Y., Suzuki K.: A Mutual-Information-Based Global Matching Method for Chest-Radiography Temporal Subtraction. Journal of Advanced Computational Intelligence and Intelligent Informatics 16: 841-850, 2012.

  • 42

    Suzuki K.: A Review of Computer-aided Diagnosis in Thoracic and Colonic Imaging. Quantitative Imaging in Medicine and Surgery 2: 163-176, 2012. (Invited, peer-reviewed)

  • 43

    He L., Chao Y., Suzuki K.: A New First-Scan Method for Two-Scan Labeling Algorithms. IEICE Transactions on Information and Systems E95-D: 2142-2145, 2012.

  • 44

    Shi Z., Li L., Zhao M., He L., Wang Y., Zhang M., and Suzuki K.: Sparse Field Snake Model: A Novel Active Contour Model Used for Lung Segmentation on Chest Radiographs. ICIC Express Letters Part B: Applications 3: 777-783, 2012.

  • 45

    Suzuki K.: Pixel-based Machine-Learning in Medical Imaging. International Journal of Biomedical Imaging 2012: Article ID 792079, 18 pages, 2012. (Invited, peer-reviewed)

  • 46

    Liao S., Penney B. C., Zhang H., Suzuki K., and Pu Y.: Prognostic Value of the Quantitative Metabolic Volumetric Measurement on 18F-FDG PET/CT in Stage IV Nonsurgical Small-cell Lung Cancer. Academic Radiology 19: 69-77, 2012. (Top 6 Hottest Article in Academic Radiology in 2012)

  • 47

    Liao S., Penney B. C., Wroblewski K., Zhang H., Simon C. A., Kampalath R., Shih M., Shimada N., Chen S., Salgia R., Appelbaum D. E., Suzuki K., Chen C., and Pu Y.: Prognostic Value of Metabolic Tumor Burden on 18F-FDG PET in Non-Surgical Patients with Non-Small Cell Lung Cancer. European Journal of Nuclear Medicine and Molecular Imaging 39: 27-38, 2012.

  • 48

    Yu Q., He L., Nakamura T., Suzuki K., Chao Y.: A Multilayered Partitioning Image Registration Method for Chest-Radiograph Temporal Subtraction. American Journal of Engineering and Technology Research 11: 2422-2427, 2011.

  • 49

    Chao Y., He L., Suzuki K.: A new connected-component labeling algorithm. American Journal of Engineering and Technology Research 11: 1099-1104, 2011.

  • 50

    Hori M., Suzuki K., Epstein M. L., and Baron R. L.: Computed Tomography Liver Volumetry Using 3-Dimensional Image Data in Living Donor Liver Transplantation: Effects of the Slice Thickness on the Volume Calculation. Liver Transplantation 17: 1427-1436, 2011.

  • 51

    Suzuki K., Epstein M. L., Kohlbrenner R., Garg S., Hori M., Oto A., and Baron R. L.: Quantitative radiology: Automated CT liver volumetry compared with interactive volumetry and manual volumetry. American Journal of Roentgenology 197: W706-W712, 2011.

  • 52

    Xu J., and Suzuki K.: Massive-training support vector regression and Gaussian process for false-positive reduction in computer-aided detection of polyps in CT colonography. Medical Physics 38: 1888-1902, 2011.

  • 53

    Chen S., Suzuki K., and MacMahon H.: Development and evaluation of a computer-aided diagnostic scheme for lung nodule detection in chest radiographs by means of two-stage nodule enhancement with support vector classification. Medical Physics 38: 1844-1858, 2011.

  • 54

    He L., Chao Y., Suzuki K., and Nakamura T.: A new first-scan strategy for raster-scan-based labeling algorithms. Journal of Information Processing Society of Japan 52: 1813-1819, 2011.

  • 55

    He L., Chao Y., and Suzuki K.: Two efficient label-equivalence-based connected-component labeling algorithms for 3-D binary images. IEEE Transactions on Image Processing 52: 1813-1819, 2011.

  • 56

    Shi Z., Bai J., Suzuki K., He L., Yao Q., and Nakamura T.: A method for enhancing dot-like regions in chest x-rays based on directional scale LoG filter, Journal of Information and Computational Science 7: 1689-1696, 2010.

  • 57

    Suzuki K., Zhang J. and Xu J.: Massive-training artificial neural network coupled with Laplacian-eigenfunction-based dimensionality reduction for computer-aided detection of polyps in CT colonography. IEEE Transactions on Medical Imaging 29: 1907-1917, 2010.

  • 58

    Lostumbo A., Suzuki K., and Dachman A. H.: Flat lesions in CT colonography. Abdominal Imaging 35: 578-583, 2010.

  • 59

    He L., Chao Y., and Suzuki K.: A run-based one-and-a-half-scan connected-component labeling algorithm. International Journal of Pattern Recognition and Artificial Intelligence 24: 557-579, 2010.

  • 60

    Suzuki K., Kohlbrenner R., Epstein M. L., Obajuluwa A. M., Xu J., and Hori M.: Computer-aided measurement of liver volumes in CT by means of geodesic active contour segmentation coupled with level-set algorithms. Medical Physics 37: 2159-2166, 2010.

  • 61

    He L., Chao Y., and Suzuki K.: An efficient first-scan method for label-equivalence-based labeling algorithms. Pattern Recognition Letters 31: 28-35, 2010.

  • 62

    Suzuki K., Rockey D. C., and Dachman A. H.: CT colonography: Advanced computer-aided detection scheme utilizing MTANNs for detection of “missed” polyps in a multicenter clinical trial. Medical Physics 30: 12-21, 2010.

  • 63

    Lostumbo A., Wanamaker C., Tsai J., Suzuki K., and Dachman A. H.: Comparison of 2D and 3D views for evaluation of flat lesions in CT colonography. Academic Radiology 17: 39-47, 2010.

  • 64

    He L., Chao Y., Suzuki K., Nakamura T., and Itoh H.: A high-speed labeling algorithm for three-dimensional binary images. Transactions of IEICE J92-D: 2261-2269, 2009.

  • 65

    Hori M., Oto A., Orrin S., Suzuki K., Baron R. L.: Diffusion-weighted MRI: a new tool for the diagnosis of fistula in ano. Journal of Magnetic Resonance Imaging 30: 1021-1026, 2009.

  • 66

    Oda S., Awai K., Suzuki K., Yanaga Y., Funama Y., MacMahon H., and Yamashita Y.: Performance of radiologists in detection of small pulmonary nodules on chest radiographs: Effect of rib suppression with a massive-training artificial neural network. American Journal of Roentgenology 193: W397–W402, 2009.

  • 67

    Suzuki K.: A supervised ‘lesion-enhancement’ filter by use of a massive-training artificial neural network (MTANN) in computer-aided diagnosis (CAD). Physics in Medicine and Biology 54: S31-S45, 2009.

  • 68

    Inaba T., He L., Suzuki K., Murakami K., and Chao Y.: A genetic-algorithm-based method for temporal subtraction of chest radiographs. Journal of Advanced Computational Intelligence and Intelligent Informatics 13: 289-296, 2009.

  • 69

    He L., Chao Y., Suzuki K., Nakamura T., and Itoh H.: A label-equivalence-based one-scan labeling algorithm. Journal of Information Processing Society of Japan 50: 1660-1667, 2009.

  • 70

    He L., Chao Y., Suzuki K., Nakamura T., and Itoh H.: A strategy for efficiency improvement of the first-scan in raster-scan-based labeling algorithms. Transactions of IEICE J92-D: 951-955, 2009.

  • 71

    He L., Chao Y., Suzuki K., and Wu K.: Fast connected-component labeling. Pattern Recognition 42: 1977-1987, 2009.

  • 72

    Shi Z., He L., Suzuki K., Nakamura T., Itoh H.: Survey of neural networks used for medical image processing. International Journal of Computer Science 3: 86-100, 2009.

  • 73

    Wu K., Otoo E., and Suzuki K.: Optimizing two-pass connected-component labeling algorithms. Pattern Analysis and Applications 12: 117-135, 2009.

  • 74

    He L., Chao Y., Suzuki K., Nakamura T., and Itoh H.: A run-based raster-scan labeling algorithm. Journal of the Institute of Image Information and Television Engineers 62: 1461-1465, 2008.

  • 75

    He L., Chao Y., Suzuki K., Nakamura T., and Itoh H.: An efficient two-scan connected-component labeling algorithm. Transactions of IEICE J91-D: 1016-1024 2008.

  • 76

    Shi Z., Chao Y., He L., Suzuki K., Nakamura T., and Itoh H.: Object location and track in image sequences by means of neural networks. International Journal of Computational Science 2: 274-285, 2008.

  • 77

    He L., Chao Y., and Suzuki K.: A run-based two-scan labeling algorithm. IEEE Transactions on Image Processing 17: 749-756, 2008.

  • 78

    Suzuki K., Yoshida H., Nappi J., Armato III S. G., and Dachman A. H.: Mixture of expert 3D massive-training ANNs for reduction of multiple types of false positives in CAD for detection of polyps in CT colonography. Medical Physics 35: 694-703, 2008.

  • 79

    King M., Giger M. L., Suzuki K., Bardo D., Greenberg B., Lan L., and Pan X.: Computerized assessment of motion-contaminated calcified plaques in cardiac multidetector CT. Medical Physics 34: 4876-4889, 2007.

  • 80

    King M., Giger M. L., Suzuki K., and Pan X.: Feature-based characterization of motion-contaminated calcified plaques in cardiac multidetector CT. Medical Physics 34: 4860-4875, 2007.

  • 81

    He L., Chao Y., Suzuki K., Shi Z., and Itoh H.: An improvement on sub-Herbrand universe computation. The Open Artificial Intelligence Journal 1: 12-18, 2007.

  • 82

    Yuan Y., Giger M. L., Li H., Suzuki K., and Sennett C.: A dual-stage method for lesion segmentation on digital mammograms. Medical Physics 34: 4180-4193, 2007.

  • 83

    Chao Y., He L., Suzuki K., Nakamura T., Shi Z., and Itoh H.: An improvement of Herbrand theorem and its application to model generation theorem proving. Journal of Computer Science and Technology 22: 541-553, 2007.

  • 84

    Muramatsu C., Li Q., Schmidt R. A., Shiraishi J., Suzuki K., Newstead G. M., and Doi K.: Determination of subjective similarity for pairs of masses and pairs of clustered microcalcifications on mammograms: comparison of similarity ranking scores and absolute similarity ratings. Medical Physics 34: 2890-2895, 2007.

  • 85

    Doshi T., Rusinak D., Halvorsen B., Rockey D. C., Suzuki K., and Dachman A. H.: CT colonography: False-negative interpretations. Radiology 244: 165-173, 2007.

  • 86

    Suzuki K., Yoshida H., Nappi J., and Dachman A. H.: Massive-training artificial neural network (MTANN) for reduction of false positives in computer-aided detection of polyps: suppression of rectal tubes. Medical Physics 33: 3814-3824, 2006.

  • 87

    Muramatsu C., Li Q., Schmidt R. A., Suzuki K., Shiraishi J., Newstead G. M., and Doi K.: Experimental determination of subjective similarity for pairs of clustered microcalcifications on mammograms: Observer study results. Medical Physics 33: 3460-3468, 2006.

  • 88

    Shiraishi J., Li Q., Suzuki K., Engelmann R., and Doi K.: Computer-aided diagnostic scheme for the detection of lung nodules on chest radiographs: localized search method based on anatomical classification. Medical Physics 33: 2642-2653, 2006.

  • 89

    Suzuki K., Abe H., MacMahon H., and Doi K.: Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN). IEEE Transactions on Medical Imaging 25: 406-416, 2006. (Ranked among the top 100 most downloaded IEEE Xplore articles in January, 2008)

  • 90

    Li F., Arimura H., Suzuki K., Shiraishi J., Li Q., Abe H., Engelmann R., Sone S., MacMahon H., and Doi K.: Computer-aided detection of peripheral lung cancers missed at CT: ROC analyses without and with localization. Radiology 237: 684-690, 2005.

  • 91

    Li Q., Li F., Suzuki K., Shiraishi J., Abe H., Engelmann R., Nie Y., MacMahon H., and Doi K.: Computer-aided diagnosis in thoracic CT. Seminars in Ultrasound, CT and MRI 26: 357-363, 2005.

  • 92

    Suzuki K., and Doi K.: How can a massive training artificial neural network (MTANN) be trained with a small number of cases in the distinction between nodules and vessels in thoracic CT? Academic Radiology 12: 1333-1341, 2005.

  • 93

    Suzuki K., Li F., Sone S., and Doi K.: Computer-aided diagnostic scheme for distinction between benign and malignant nodules in thoracic low-dose CT by use of massive training artificial neural network. IEEE Transactions on Medical Imaging 24: 1138-1150, 2005.

  • 94

    Muramatsu C., Li Q., Suzuki K., Schmidt R. A., Shiraishi J., Newstead G. M., and Doi K.: Investigation of psychophysical measure for evaluation of similar images for mammographic masses: Preliminary results. Medical Physics 32: 2295-2304, 2005.

  • 95

    Suzuki K., Shiraishi J., Abe H., MacMahon H., and Doi K.: False-positive reduction in computer-aided diagnostic scheme for detecting nodules in chest radiographs by means of massive training artificial neural network. Academic Radiology 12: 191-201, 2005.

  • 96

    Suzuki K.: Determining the receptive field of a neural filter. Journal of Neural Engineering 1: 228-237, 2004.

  • 97

    Li F., Aoyama M., Shiraishi J., Abe H., Li Q., Suzuki K., Engelmann R., Sone S., MacMahon H., and Doi K.: Radiologists’ performance for differentiating benign from malignant lung nodules on high-resolution CT using computer-estimated likelihood of malignancy. American Journal of Roentgenology 183: 1209-1215, 2004.

  • 98

    Arimura H., Katsuragawa S., Suzuki K., Li F., Shiraishi J., Sone S., and Doi K.: Computerized scheme for automated detection of lung nodules in low-dose computed tomography images for lung cancer screening. Academic Radiology 11: 617-629, 2004.

  • 99

    Suzuki K., Horiba I., Sugie N., and Nanki M.: Extraction of left ventricular contours from left ventriculograms by means of a neural edge detector. IEEE Transactions on Medical Imaging 23: 330-339, 2004.

  • 100

    Suzuki K., Horiba I., and Sugie N.: Neural edge enhancer for supervised edge enhancement from noisy images. IEEE Transactions on Pattern Analysis and Machine Intelligence 25: 1582-1596, 2003.

  • 101

    Uchiyama Y., Katsuragawa S., Abe H., Shiraishi J., Li F., Li Q., Zhang C., Suzuki K., and Doi K.: Quantitative computerized analysis of diffuse lung disease in high-resolution computed tomography. Medical Physics 30: 2440-2454, 2003.

  • 102

    Suzuki K., Armato III S. G., Li F., Sone S., and Doi K.: Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography. Medical Physics 30: 1602-1617, 2003. (Selected and published in an edited compilation, Virtual Journal of Biological Physics Research 6: 1, July 2003)

  • 103

    Suzuki K., Horiba I., Sugie N., and Nanki M.: Contour extraction of the left ventricular cavity from digital subtraction angiograms using a neural edge detector. Systems and Computers in Japan 34: 55-69, 2003.

  • 104

    Suzuki K., Horiba I., and Sugie N.: Linear-time connected-component labeling based on sequential local operations. Computer Vision and Image Understanding 89: 1-23, 2003. (Awarded Top 16 of Most Downloaded Articles Award)

  • 105

    Suzuki K., Horiba I., Sugie N., and Nanki M.: Neural filter with selection of input features and its application to image quality improvement of medical image sequences. IEICE Transactions on Information and Systems E85-D: 1710-1718, 2002.

  • 106

    Suzuki K., Horiba I., and Sugie N.: Efficient approximation of neural filters for removing quantum noise from images. IEEE Transactions on Signal Processing 50: 1787-1799, 2002.

  • 107

    Suzuki K., Horiba I., and Sugie N.: A simple neural network pruning algorithm with application to filter synthesis. Neural Processing Letters 13: 43-53, 2001.

  • 108

    Suzuki K., Horiba I., and Sugie N.: An approach to synthesize filters with reduced structures using a neural network. Quantum Information 2: 205-218, 2000.

  • 109

    Suzuki K., Horiba I., Ikegaya K., and Nanki M.: Recognition of coronary arterial stenosis using neural network on DSA system. Systems and Computers in Japan 26: 66-74, 1995.

  • 110

    Hirano Y., Ito T., Hashimoto N., Kido S., and Suzuki K.: Massive-training artificial neural network deep learning in computer-aided diagnosis for chest and abdomen. Medical Imaging Technology 35(4): 194-199, 2017. (Invited, peer-reviewed)

  • 111

    Suzuki K.: Supervised nonlinear image processing based on artificial neural networks: Basic principle of neural image processing and its applications. Japanese Journal of Nuclear Medicine Technology 24: 433-442, 2004.

  • 112

    Suzuki K., Horiba I., and Sugie N.: Detection of edges in noisy images using a neural edge detector. Transactions of IEICE J86-D-II: 579-583, 2003.

  • 113

    Ninagawa K., Umeyama T., Suzuki K., and Sugie N.: Sound source separation in the frequency domain with image processing. Transactions of Institute of Electrical Engineers of Japan 121-C: 1866-1874, 2001. (Awarded Best Paper Award for Young Researchers)

  • 114

    Suzuki K., Horiba I., Sugie N., and Nanki M.: Neural filter with selection of input features for improving image quality of medical x-ray image sequences. Journal of Information Processing Society of Japan 42: 2176-2188, 2001.

  • 115

    Suzuki K.: Studies on neural image processing for medical x-ray images. PhD Thesis, Graduate School of Engineering, Nagoya University, 1503, 2001.

  • 116

    Suzuki K., Horiba I., and Sugie N.: Fast connected-component labeling through sequential local operations in the course of forward raster scan followed by backward raster scan. Journal of Information Processing Society of Japan 41: 3070-3081, 2000.

  • 117

    Suzuki K., Horiba I., Sugie N., and Nanki M.: Contour extraction of left ventricles in DSA images by means of neural edge detector. Transactions of IEICE J83-D-II: 2017-2029, 2000.

  • 118

    Suzuki K., Horiba I., and Sugie N.: An analysis of the neural filter trained to improve quality of images with quantum noise and realization of approximate filter. Journal of Information Processing Society of Japan 41: 711-721, 2000.

  • 119

    Suzuki K., Horiba I., and Sugie N.: A method for determining reduced structure of a neural filter. Journal of Information Processing Society of Japan 40: 4226-4238, 1999.

  • 120

    Suzuki K., Hayashi T., Ikeda S., Horiba I., and Sugie N.: Improving image quality of medical low-dose x-ray image sequences using a neural filter. Transactions of Institute of Electrical Engineers of Japan 119-C: 1383-1391, 1999.

  • 121

    Ueda K., Yamada M., Horiba I., Ikegaya K., and Suzuki K.: A direct estimation method of occupancy rate in parking lot using analogue output neural network model. Journal of Information Processing Society of Japan 36: 627-635, 1995.

  • 122

    Suzuki K., Horiba I., Ikegaya K., and Nanki M.: Recognition of degree of stenosis using neural network on coronary arterial DSA system. Transactions of IEICE J77-D-II: 1910-1916, 1994.

Refereed Papers in Conference Proceedings

ID

Title

  • 1

    Kodera S., Rahmaniar W., Oshibe H., Jin Z., Watadani T., Abe O. and Suzuki K.: Super-Efficient Lung Nodule Classification Using Massive-Training Artificial Neural Network (MTANN) Compact Model on LIDC-IDRI Database, 2024 6th International Conference on Image, Video and Signal Processing (IVSP 2024), Kawasaki, Japan, March 14-16, 2024.

  • 2

    Yang Y., Jin Z., Nakatani F., Miyake M., and Suzuki K.: Development of a small-data deep-learning model based on an MTANN for soft tissue sarcoma diagnosis in MRI. Program of Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA), T5A-SPIN-4, November 2023.

  • 3

    Jin Z., Pang M., Qu T., Oshibe H., Sasage R., and Suzuki K.: Feature Map Visualization for Explaining Black-Box Deep Learning Model in Liver Tumor Segmentation. Program of Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA), T5A-SPPH-12, November 2023.

  • 4

    Yang S., Xiang M., Qu T., Jin Z., and Suzuki K.: Reconstruction of Fast Acquisition MRI with Under-sampled K-space Data by Using Massive-Training Artificial Neural Networks (MTANNs). Program of Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA), T5A-SPPH-11, November 2023.

  • 5

    Yang Y., Jin Z., and Suzuki K.: Federated learning – Game changing AI concept to train AI without sending patient data out from hospitals. Program of Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA), INEE-31, November 2023.

  • 6

    Sirisanwannakul K., Siripool N., Suzuki K., Kongprawechnon W., and Karnjana J.: Detection and Correction of Defective Relative Humidity Data Collected from the Greenhouse Environment Using Nested Kalman Filters with Standard Deviation Analysis, 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC 2023), Taipei, Taiwan, October 31- November 3, 2023.

  • 7

    Jin Z., Pang M., Yang Y., Mahdi F. P., Qu T., Sasage R., and Suzuki K.: Explaining Massive-Training Artificial Neural Networks in Medical Image Analysis Task through Visualizing Functions within the Models. The 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023), Vancouver, Canada, October 2023.

  • 8

    Suzuki K.: ROC-Score-Based Ensemble Training for Multiple Deep Learning Modules in Classification between Polyps and Non-Polyps in CT Colonography, 2023 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC), Oahu, Hawaii, October 1-4, 2023.

  • 9

    Deng Z., Jin Z., and Suzuki K.: Radiation Dose Reduction in Digital Breast Tomosynthesis by MTANN with Multi-scale Kernels, 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2023), Sydney, Australia, July 24-27, 2023.

  • 10

    Pang M., Jin Z., Qu T., Mahdi F. P., Sasage R., and Suzuki K.: Functional Model Visualization for Explaining Massive-Training Artificial Neural Network for Liver Tumor Segmentation, 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2023), Sydney, Australia, July 24-27, 2023.

  • 11

    Yang S., Xiang M., Qu T., Jin Z., and Suzuki K.: Under-sampled Image Reconstruction in Fast Acquisition MRI with Massive-Training Artificial Neural Networks (MTANNs) Deep Learning Approach, 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2023), Sydney, Australia, July 24-27, 2023.

  • 12

    Yang Y., Jin Z., Nakatani F., Miyake M., and Suzuki K.: AI-aided Diagnosis of Rare Soft-Tissue Sarcoma by Means of Massive-Training Artificial Neural Network (MTANN), 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2023), Sydney, Australia, July 24-27, 2023.

  • 13

    Suzuki K.: Deep Residual Massive-Training Artificial Neural Network for Image Denoising, 2023 5th International Conference on Image, Video and Signal Processing (IVSP 2023), Singapore, March 24-26, 2023.

  • 14

    Xu L., Mahdi F. P., Jin Z., Noguchi Y., Murata M., and Suzuki K.: Generating simulated fluorescence images for enhancing proteins from optical microscopy images of cells using massive-training artificial neural networks. Proc. SPIE Medical Imaging (SPIE MI), San Diego, USA, February 2023.

  • 15

    You J., Li D., Okumura M., and Suzuki K.: JPG – Jointly Learn to Align: Automated Disease Prediction and Radiology Report Generation. The 29th International Conference on Computational Linguistics (COLING 2022), Gyeongju, South Korea, October 2022.

  • 16

    Deng Z., Yang Y., Jin Z., Suzuki K., FedAL: An Federated Active Learning Framework for Efficient Labeling in Skin Lesion Analysis. International Conference on Systems, Man, and Cybernetics (IEEE SMC 2022), Prague, Czech, October 2022.

  • 17

    Yang Y., Jin Z., and Suzuki K.: Federated Tumor Segmentation with Patch-wise Deep Learning Model. 25th International Conference on Medical Image Computing and Computer Assisted InterventionInternational (MICCAI)
    Workshop on machine learning in medical imaging (MLMI)
    , Singapore, September 2022.

  • 18

    Suzuki K.: Small data deep learning for lung cancer detection and diagnosis in CT. Proceedings of IEEE International Conference on Big Data Computing Service and Machine Learning Applications (IEEE BigDataService 2022), Fremont, USA, August 2022.

  • 19

    Yang Y., Jin Z., and Suzuki K.: Federated Learning Coupled with Massive-Training Artificial Neural Networks in Tumor Segmentation in CT Images. The 44th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2022), WeEP-35.8, July 2022.

  • 20

    Wang L., Qi J., Cheng J., and Suzuki K.: Action unit detection by exploiting spatial-temporal and label-wise attention with transformer. Proceedings of 3rd Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW 2022), held in conjunction with the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2470-2475, New Orleans, USA, June 2022.

  • 21

    Suzuki K.: Generating simulated higher-dose CT images from ultra-low-dose CT images by means of massive-training artificial neural networks. IUPESM World Congress on Medical Physics and Biomedical Engineering (IUPESM WC2022), June 2022.

  • 22

    Wang L., Wang S., Qi J., and Suzuki K.: A Multi-task Mean Teacher for Semi-supervised Facial Affective Behavior Analysis. Proceedings of the IEEE/CVF International Conference on Computer Vision, 3603-3608, October 2021.

  • 23

    Sato M., Yang Y., Jin Z., and Suzuki K.: Segmentation of Liver Tumor in Hepatic CT by Using MTANN Deep Learning with Small Training Dataset Size. The 6th International Symposium on Biomedical Engineering (ISBE2021), December 2021.

  • 24

    Onai Y., Mahdi F. P., Jin Z., and Suzuki K.: Virtual High-Radiation-Dose Image Generation from Low-Radiation-Dose Image in Digital Breast Tomosynthesis (DBT) Using Massive-Training Artificial Neural Network (MTANN). The 6th International Symposium on Biomedical Engineering (ISBE2021), December 2021.

  • 25

    Xiang M., Jin Z., and Suzuki K.: Massive-Training Artificial Neural Network (MTANN) for Image Quality Improving in Fast-Acquisition MRI of the Knee. The 6th International Symposium on Biomedical Engineering (ISBE2021), December 2021.

  • 26

    Xiang M., Jin Z., and Suzuki K.: Massive-Training Artificial Neural Network (MTANN) with Special Kernel for Artifact Reduction In Fast-Acquisition MRI of the Knee. 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), 1210-1213, May 2021.

  • 27

    Sato M., Jin Z., and Suzuki K.: Semantic Segmentation of Liver Tumor in Contrast-enhanced Hepatic CT by Using Deep Learning with Hessian-based Enhancer with Small Training Dataset Size. 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), 34-37, May 2021.

  • 28

    Yuan T., Jin Z., Tokuda Y., Naoi Y., Tomiyama N., Obi T., and Suzuki K.: Prediction of genetically-evaluated tumour responses to chemotherapy from breast MRI using machine learning with model selection. Journal of Physics: Conference Series, vol. 1780, 012040, February 2021.

  • 29

    Yuan T., Jin Z., Tokuda Y., Naoi Y., Tomiyama N., and Suzuki K.: MR Imaging biomarkers for Prediction of Genetic Assessment for Breast Cancer Recurrence: A Radiogenomics Study. IEICE Technical Report, MI2019-118, pp. 227-230, Okinawa, January 2020.

  • 30

    Wang Y., Jin Z., Tokuda Y., Naoi Y., Tomiyama N., Suzuki K.: Neural Network Convolution Deep Learning for Semantic Segmentation of Breast Tumor in MRI. Proc. of 4th International Symposium on Biomedical Engineering (ISBE2019), pp. 286-287, P2-43, Nov 2019.

  • 31

    Yuan T., Jin Z., Tokuda Y., Naoi Y., Tomiyama N., Suzuki K.: Discovery of MR Imaging Biomarkers for Prediction of Pathological Complete Responses to Chemotherapy for Breast Cancer. Proc. of 4th International Symposium on Biomedical Engineering (ISBE2019), pp. 274-275, P2-37, Nov 2019.

  • 32

    Wang Y., Jin Z., Tokuda Y., Naoi Y., Tomiyama N., and Suzuki K.: Development of Deep-learning Segmentation for Breast Cancer in MR Images based on Neural Network Convolution. International Conference on Computing and Pattern Recognition (ICCPR 2019), pp. 187-191, J073, Beijing, China, October 2019.

  • 33

    Martínez-García M., Zhang Y., Suzuki K., and Zhang Y.: Measuring System Entropy with a Deep Recurrent Neural Network Model. Proc. 2019 IEEE 17th International Conference on Industrial Informatics (INDIN), pp. 1253-1256, Helsinki, Finland, July 2019.

  • 34

    Zarshenas A., Liu J., Forti P., and Suzuki K.: Mixture of Deep-Learning Experts for Separation of Bones from Soft Tissue in Chest Radiographs. IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2018), pp. 1321–1326, Miyazaki, Japan, October 2018.

  • 35

    Zarshenas A., and Suzuki K.: Deep Neural Network Convolution for Natural Image Denoising. IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2018), pp. 2534-2539, Miyazaki, Japan, October 2018.

  • 36

    Zarshenas A., Zhao Y., Liu J., Higaki T., Fukumoto W., Awai K., and Suzuki K.: Deep 3D Anatomy-Specific Neural Network Convolution for Radiation Dose Reduction in Chest CT at a Micro-Dose Level. Proc. International Conference on IEEE Engineering in Medicine & Biology Society (IEEE EMBC), ThPoS-23.22, July 2018.

  • 37

    Liu J., Zarshenas A., Wei Z., Yang L., Fajardo L., and Suzuki K.: Sequential Neural Network Convolution (NNC) Deep Learning in Radiation Dose Reduction in Digital Breast Tomosynthesis (DBT): Preliminary Results. Proc. International Conference on IEEE Engineering in Medicine & Biology Society (IEEE EMBC), ThPoS-23.23, July 2018.

  • 38

    Liu J., Zarshenas A., Qadir S., Yang L., Fajardo L., and Suzuki K.: Radiation dose reduction in digital breast tomosynthesis (DBT) by means of neural network convolution (NNC) deep learning. Proc. International Workshop on Breast Imaging (IWBI), Atlanta, GA, July 2018.

  • 39

    Makkinejad N., Tajbakhsh N., Zarshenas A., Khokhar A., and Suzuki K.: Reduction in training time of a deep learning (DL) model in radiomics analysis of lesions in CT. Proc. SPIE Medical Imaging (SPIE MI), Huston, TX, February 2018.

  • 40

    Hashimoto N., Suzuki K., Liu J., Hirano Y., MacMahon H., and Kido S.: Deep neural network convolution (NNC) for three-class classification of diffuse lung disease opacities in high-resolution CT (HRCT): Consolidation, ground-glass opacity (GGO), and normal opacity. Proc. SPIE Medical Imaging (SPIE MI), Huston, TX, February 2018.

  • 41

    Ito T, Hirano Y, Kido S., Kim H., and Suzuki K.: Computerized system for classification between benign and malignant lung nodules in thoracic CT by means of deep learning: neural network convolution (NNC) and convolutional neural network (CNN). SPIE Medical Imaging (SPIE MI), Huston, TX, February 2018.

  • 42

    Liu J., Zarshenas A., Wei Z., Yang L., Fajardo L., and Suzuki K.: Radiation dose reduction in digital breast tomosynthesis (DBT) by means of deep-learning-based supervised image processing. Proc. SPIE Medical Imaging (SPIE MI), Huston, TX, February 2018.

  • 43

    Suzuki K., Liu J., Zarshenas A., Higaki T., Fukumoto W., and Awai K.: Neural Network Convolution (NNC) for Converting Ultra-Low-Dose to “Virtual” High-Dose CT Images. Lecture Notes in Computer Science, Machine Learning in Medical Imaging (MLMI) (Springer-Verlag, Berlin), Quebec, Canada, September 2017.

  • 44

    Suzuki K., Zarshenas M., Liu J., Fan Y., Makkinejad N., Forti P., and Dachman A.: Development of computer-aided diagnostic (CADx) system for distinguishing neoplastic from non-neoplastic lesions in CT colonography (CTC): Toward CTC beyond Detection, Proc. IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2015), pp. 2262-2266, Hong Kong, October 2015.

  • 45

    Suzuki K.: Patch-based Machine Learning and Deep Learning in Medical Image Processing and Diagnosis Proc. 4th International Conference on Informatics, Electronics & Vision (ICIEV 2015), pp. 42-43, Kitakyushu, Japan, June 2015.

  • 46

    Suzuki K.: Mining of Training Samples for Multiple Learning Machines in Computer-Aided Detection of Lesions in CT Images. Proc. IEEE ICDM Workshop on Data Mining in Medical Imaging (DMMI), Shenzhen, China, December 2014.

  • 47

    Suzuki K.: Mining of Training Samples for Multiple Learning Machines in Computer-Aided Detection of Lesions in CT Images. Proc. IEEE ICDM Workshop on Data Mining in Medical Imaging (DMMI), Shenzhen, China, December 2014.

  • 48

    Tanaka R., Sanada S., Oda M., Mitsutaka M., Suzuki K., Sakuta K., and Kawashima H.: Quantitative analysis of rib movement based on dynamic chest bone images: preliminary results. Proc. SPIE Medical Imaging (SPIE MI), San Diego, CA, February 2014.

  • 49

    Calabrese D., Zhou K., Liu Y., and Suzuki K.: Improved Segmentation of Liver in CT with Massive-Training Artificial Neural Network (MTANN) Liver Enhancer. Proc. IEEE Engineering in Medicine and Biology Conference (IEEE EMBC), Short Papers No. 3331, Osaka, Japan, July 2013.

  • 50

    Suzuki K., Huynh H. T., Liu Y., Calabrese D., Zhou K., Oto A., and Hori M.: Computerized Segmentation of Liver in Hepatic CT and MRI by Means of Level-Set Geodesic Active Contouring. Proc. IEEE Engineering in Medicine and Biology Conference (IEEE EMBC), pp. 2984-2987, Osaka, Japan, July 2013 (Invited).

  • 51

    He L., Chao Y., Yang Y., Li S., and Suzuki K.: A Novel Two-Scan Connected-Component Labeling Algorithm. IAENG Transactions on Engineering Technologies, Lecture Notes in Electrical Engineering 229: 445-459, 2013.

  • 52

    Suzuki K., Hori M., Iinuma G., and Dachman A. H.: Effect of CADe on Radiologists’ Performance in Detection of “Difficult” Polyps in CT Colonography. Proc. SPIE Medical Imaging (SPIE MI) 8670: 8670x-1-x, Orlando, FL, February 2013.

  • 53

    He L., Chao Y., Suzuki K.: A New Algorithm for Labeling Connected-Components and Calculating the Euler Number, Connected-Component Number, and Hole Number, Proc. Int. Conf. Pattern Recognition (ICPR), pp.3099-3102, Tsukuba, Japan, November 2012.

  • 54

    Cheng S., Suzuki K.: Bone Suppression in Chest Radiographs by Means of Anatomically Specific Multiple Massive-Training ANNs, Proc. Int. Conf. Pattern Recognition (ICPR), pp.17-20, Tsukuba, Japan, November 2012.

  • 55

    Yu Q., He L., Chao Y., Suzuki K., Nakamura T.: A mutual-information-based image registration method for chest-radiograph temporal subtraction. International Conference on Computer Science and Information Processing (CSIP), pp.1098-1101, Xi’an, Shaanxi, China, August 2012.

  • 56

    Yu Q., He L., Nakamura T., Suzuki K., Chao Y.: A multilayered partitioning image registration method for chest-radiograph temporal subtraction. International Conference on Computer Science and Information Processing (CSIP), 181-184, Xi’an, Shaanxi, China, August 2012.

  • 57

    He L., Chao Y., Suzuki K.: A New Two-Scan Algorithm for Labeling Connected Components in Binary Images, Proc. World Congress on Engineering2: 1141-1146, London, UK, July 2012.

  • 58

    Xu J., and Suzuki K.: Maximal Partial AUC Feature Selection in Computer-Aided Detection of Hepatocellular Carcinoma in Contrast-Enhanced Hepatic CT. Proc. SPIE Medical Imaging (SPIE MI), 8315: 83150H-1-7, San Diego, CA, February 2012.

  • 59

    Xu J., and Suzuki K.: Computer-aided Detection of Polyps in CT Colonography By Means of AdaBoost. Proc. SPIE Medical Imaging (SPIE MI)8315: 83150V-1-7, San Diego, CA, February 2012.

  • 60

    Chao Y., He L., and Suzuki K.: A new connected-component labeling algorithm. Proceedings of 2011 International Conference on Opto-Electronics Engineering and Information Science (ICOEIS2011), Xi’an, China, December 2011.

  • 61

    He L., Chao Y., Suzuki K.: A Labeling Algorithm for Connected Components and Holes. Proceedings of 2011 International Conference on Opto-Electronics Engineering and Information Science (ICOEIS2011), Xi’an, China, December 2011.

  • 62

    Xu J. and Suzuki K.: False-positive reduction in computer-aided detection of polyps in CT colonography: a massive-training support vector regression approach. Lecture Notes in Computer Science, Virtual Colonoscopy and Abdominal Imaging 6668: 47-52 (Springer-Verlag, Berlin), 2011.

  • 63

    Suzuki K.: Recent Advances in Reduction of False Positive in Computerized Detection of Polyps in CT Colonography. Lecture Notes in Computer Science, Virtual Colonoscopy and Abdominal Imaging 6668: 32-39 (Springer-Verlag, Berlin), 2011. (Invited)

  • 64

    Xu J. and Suzuki K.: Computer-Aided Detection of Polyps in CT Colonography with Pixel-based Machine Learning Techniques, Lecture Notes in Computer Science, Machine Learning in Medical Imaging (MLMI) 7009: 360-367 (Springer-Verlag, Berlin), Toronto, Canada, September 2011.

  • 65

    Suzuki K.: Computer-aided diagnosis – research, development, commercialization and clinical implementation. Proceedings of Workshop on Fusion of Information Technology and Medicine, pp. 5-14, Shiga, Japan, August 2011.

  • 66

    Xu J., and Suzuki K.: Computer-aided detection of hepatocellular carcinoma in hepatic CT: False positive reduction with feature selection, Proc. IEEE International Symposium on Biomedical Imaging (IEEE ISBI), 1097-1100, Chicago,IL, March 2011.

  • 67

    Ferraro F., Kawaler E., and Suzuki K.: A spinning tangent based CAD system for detection of flat lesions in CT colonography. Proc. IEEE International Symposium on Biomedical Imaging (IEEE ISBI), pp.156-159, Chicago,IL, March 2011.

  • 68

    Yu Q., He L., Nakamura T., Suzuki K., Chao Y.: A Mutual-Information-Based Image Registration Method for Chest-Radiograph Temporal Subtraction, 2011 3rd IEEE International Conference on Computer and Network Technology (ICCNT 2011), V13-359- V13-362, Taiyuan, China, February 2011.

  • 69

    Chen S., Suzuki K., and MacMahon H.: Improved computerized detection of lung nodules in chest radiographs by means of “virtual dual-energy” radiography. Proc. SPIE Medical Imaging (SPIE MI) 7963: 79630C-1-9, Orlando, FL, February 2011.

  • 70

    Xu J., Suzuki K., Hori M., Oto A., and Baron R.: Computer-aided detection of hepatocellular carcinoma in multiphase contrast-enhanced hepatic CT: a preliminary study. Proc. SPIE Medical Imaging (SPIE MI) 7963: 79630S-1-6, Orlando, FL, February 2011.

  • 71

    Suzuki K., Armato S. G., Engelmann R., Caligiuri P., and MacMahon H.: Temporal subtraction of “virtual dual-energy” chest radiographs for improved conspicuity of growing cancers and other pathologic changes. Proc. SPIE Medical Imaging (SPIE MI) 7963: 79630F-1-6, Orlando, FL, February 2011.

  • 72

    Xu J. and Suzuki K.: False-positive reduction in computer-aided detection of polyps in CT colonography: a massive-training support vector regression approach. Proc. MICCAI 2010 Workshop on Computational Challenges and Clinical Opportunities in Virtual Colonoscopy and Abdominal Imaging, pp.55-60, Beijing, China, September 2010.

  • 73

    Suzuki K.: Recent Advances in Reduction of False Positives in Computerized Detection of Polyps in CT Colonography. Proc. MICCAI 2010 Workshop on Computational Challenges and Clinical Opportunities in Virtual Colonoscopy and Abdominal Imaging, pp.32-39, Beijing, China, September 2010. (Invited)

  • 74

    Suzuki K., Xu J.,Zhang J., and Sheu I.: Principal-component massive-training machine-learning regression for false-positive reduction in computer-aided detection of polyps in CT colonography. Lecture Notes in Computer Science, Machine Learning in Medical Imaging (MLMI) 6357: 182-189 (Springer-Verlag, Berlin), Beijing, China, September 2010.

  • 75

    He L., Inaba T., Suzuki K., Murakami K., Chao Y., Tang W., Shi Z., and Nakamura T.: A global registration method for temporal subtraction of chest radiographs. Proc. 2010 International Conference on Image Processing and Pattern Recognition in Industrial Engineering 7820: 78202A1-78202A8, Xian, Shaanxi, China, August 2010.

  • 76

    He L., Chao Y., Suzuki K., Tang W., Shi Z., and Nakamura T.: An efficient run-based connected-component labeling algorithm for three-dimensional binary images. Proc. 2010 International Conference on Image Processing and Pattern Recognition in Industrial Engineering 7820: 7820291-7820298, Xian, Shaanxi, China, August 2010.

  • 77

    Shi Z., Suzuki K., and He L.: A filtering method for enhancing dot-like regions in chest x-rays. Proc. the 8th International Bioinformatics Workshop (IBW2010), Wuhan, China, June 2010.

  • 78

    Suzuki K., Epstein M. L., Xu J., Obara P. R., Rockey D. C., and Dachman A. H.: Automated scheme for measuring polyp volume in CT colonography using Hessian matrix-based shape extraction and 3D volume growing. Proc. SPIE Medical Imaging (SPIE MI) 7624: 762423-1-6, San Diego, CA,February 2010.

  • 79

    Suzuki K., Epstein M. L., Kohlbrenner R., Obajuluwa A. M., Xu J., Hori M., and Baron R.: CT liver volumetry using geodesic active contour segmentation with a level-set algorithm. Proc. SPIE Medical Imaging (SPIE MI) 7624: 76240R-1-6, San Diego, CA,February 2010.

  • 80

    Shi Z., Suzuki K., and He L.: Reducing FPs in nodule detection using neural networks ensemble. Second International Symposium on Information Science and Engineering (ISISE), pp. 331-333, Shanghai, China, December 2009.

  • 81

    He L., Chao Y., Suzuki K., and Itoh H.: A fast first-scan algorithm for label-equivalence-based connected-component labeling. Proc. IEEE International Conference on Image Processing (IEEE ICIP), pp. 4013-4016 Cairo, Egypt, November 2009.

  • 82

    Suzuki K., Hori M., McFarland E. G., Friedman A. C., Iinuma G., Rockey D. C., and Dachman A. H.: Observer performance study with CAD in detection of polyps in false-negative cases: Preliminary results. Proc. International Symposium on Virtual Colonoscopy (ISVC), p. 135, Reston, VA, October 2009.

  • 83

    He L., Chao Y., Suzuki K., and Itoh H.: A run-based one-scan labeling algorithm. Lecture Notes in Computer Science, Image Analysis and Recognition (ICIAR) 5627: 93-102, (Springer-Verlag, Berlin), Halifax, Canada, July 2009.

  • 84

    Suzuki K., Sheu I., Rockey D. C., and Dachman A. H.: A CAD utilizing 3D massive-training ANNs for detection of flat lesions in CT colonography: Preliminary results. Proc. SPIE Medical Imaging (SPIE MI) 7260: 72601A-1-7, Orlando, FL, February 2009.

  • 85

    Suzuki K.: Segmentation of lesions with improved specificity in computer-aided diagnosis using a massive-training artificial neural network (MTANN). Proc. Int. Conf. Machine Learning and Applications (ICMLA), pp. 523-527, San Diego, CA, December 2008.

  • 86

    Suzuki K., Shi Z., and Zhang J.: Supervised enhancement lung nodules by use of a massive-training artificial neural network (MTANN) in computer-aided diagnosis (CAD). Proc. Int. Conf. Pattern Recognition (ICPR), MoCT6.3, 4 pages, Tampa, FL, December 2008.

  • 87

    Suzuki K., Sheu I., Rockey D. C., and Dachman A. H.: Detection of flat lesions: Performance of a CAD utilizing 3D massive-training ANNs on a cohort from a large multicenter clinical trial. Proc. International Symposium on Virtual Colonoscopy (ISVC), pp. 152-153, Boston, MA, October 2008.

  • 88

    Suzuki K., Epstein M. L., Kuo J., Obara P. R., Rockey D. C., and Dachman A. H.: CT colonography polyp volumetrics: Fully automated scheme for measuring polyp volume using 3D volume-growing and sub-voxel refinement techniques. Proc. International Symposium on Virtual Colonoscopy (ISVC), pp. 149-150, Boston, MA, October 2008.

  • 89

    Inaba T., He L., Chao Y., Suzuki K., and Murakami K: A genetic-algorithm-based method for temporal subtraction in chest radiography. Proc. Joint 4th International Conference on Soft Computing and Intelligent Systems and 9th International Symposium on advanced Intelligent Systems (SCIS & ISIS), pp. 1619-1624, Nagoya, Japan, September 2008.

  • 90

    He L., Chao Y., Suzuki K., Nakamura T., and Itoh H.: A survey of labeling algorithms. Proc. Joint 4th International Conference on Soft Computing and Intelligent Systems and 9th International Symposium on advanced Intelligent Systems (SCIS & ISIS), pp.1293-1298, Nagoya, Japan, September 2008 (Invited).

  • 91

    Suzuki K., Epstein M. L., Sheu I., Kohlbrenner R., Rockey D. C., and Dachman A. H.: Massive-training artificial neural networks for cad for detection of polyps in CT colonography: False-negative cases in a large multicenter clinical trial. Proc. IEEE International Symposium on Biomedical Imaging (IEEE ISBI), pp. 684-687, Paris, France, May 2008.

  • 92

    Suzuki K., Sheu I., Epstein M. L., Kohlbrenner R., Lostumbo A., Rockey D. C., and Dachman A. H.: An MTANN CAD for detection of polyps in false-negative CT colonography cases in a large multicenter clinical trial: Preliminary results. Proc. SPIE Medical Imaging (SPIE MI) 6915: 69150F-1-7, San Diego, CA, February 2008.

  • 93

    Rodgers Z. B., King M. T., Giger M. L., Bardo D., Vannier M. W., Lan L., and Suzuki K.: Computerized assessment of coronary calcified plaques in CT images of a dynamic cardiac phantom. Proc. SPIE Medical Imaging (SPIE MI) 6915: 69150M-1-6, San Diego, CA, February 2008.

  • 94

    King M., Giger M. L., Suzuki K., and Pan X.: Image quality evaluation of motion-contaminated calcified plaques in cardiac CT. IEEE Nuclear Science Symposium Conference Record, pp. 2717-2720, Honolulu, HI, October 2007.

  • 95

    Lostumbo A., Tsai J., Suzuki K., and Dachman A. H.: Comparison of 2D and 3D views for measurement and conspicuity of flat lesions in CT colonography. Proc. International Symposium on Virtual Colonoscopy (ISVC), pp. 120-121, Boston, MA, October 2007.

  • 96

    Suzuki K., Sheu I., Epstein M. L., Verceles J., Rockey D. C., and Dachman A. H.: Performance of CAD based on MTANNs for detection of false-negative polyps in a multicenter clinical trial. Proc. International Symposium on Virtual Colonoscopy (ISVC), pp. 93-94, Boston, MA, October 2007.

  • 97

    He L., Chao Y., and Suzuki K.: A linear-time two-scan labeling algorithm. Proc. IEEE International Conference on Image Processing (IEEE ICIP), V: 241-244, San Antonio, TX, September 2007.

  • 98

    He L., Chao Y., and Suzuki K.: A run-based two-scan labeling algorithm. Lecture Notes in Computer Science, International Conference on Image Analysis and Recognition (ICIAR) 4633: 131-142 (Springer-Verlag, Berlin), Montreal, Canada, August 2007.

  • 99

    Muramatsu C., Li Q., Schmidt R. A., Shiraishi J., Suzuki K., Newstead G. M., and Doi K.: Determination of subjective and objective similarity for pairs of masses on mammograms for selection of similar images. Proc. SPIE Medical Imaging (SPIE MI) 6514, 65141I-1-9, February 2007.

  • 100

    King M., Pan X., Giger M. L., and Suzuki K.: Motion compensated reconstructions of calcified coronary plaques in cardiac CT. Proc. SPIE Medical Imaging (SPIE MI) 6510, 651012-1-6, February 2007.

  • 101

    King M., Giger M. L., Suzuki K., and Pan X.: Computer-aided assessment of cardiac computed tomography images. Proc. SPIE Medical Imaging (SPIE MI) 6514: 65141B-1-6, February 2007.

  • 102

    Suzuki K., He L., Khankari S., Ge L., Verceles J., and Dachman A. H.: Mixture of expert artificial neural networks with ensemble training for reduction of various sources of false positives in CAD. Proc. SPIE Medical Imaging (SPIE MI) 6514: 651401-1-6, February 2007.

  • 103

    Li H., Giger M. L., Yuan Y., Lan L., Suzuki K., Jamieson A. R., Yarusso L., Nishikawa R. M., and Sennett C.: Comparison of computerized image analyses for digitized screen-film mammograms and full-field digital mammography images. Lecture Notes in Computer Science, Digital Mammography 4046: 569-575 (Springer-Verlag, Berlin), Manchester, UK, June 2006.

  • 104

    Suzuki K., Li F., Li Q., MacMahon H., and Doi K.: Comparison between 2D and 3D massive-training ANNs (MTANNs) in CAD for lung nodule detection on MDCT. International Journal of Computer Assisted Radiology and Surgery 1(p): 354-355, January 2006.

  • 105

    Yuan Y., Giger M. L., Suzuki K., Li H., and Jamieson A. R.: A two-stage method for lesion segmentation on digital mammograms. Proc. SPIE Medical Imaging (SPIE MI) 6144: 3W1-5, February 2006. (Awarded Honorable Mention Poster Award)

  • 106

    Wu K., Otoo E., and Suzuki K.: Two Strategies to speed up connected component labeling algorithms. Lawrence Berkeley National Laboratory Tech Report LBNL-59102, January 2005.

  • 107

    Suzuki K., Li F., Aoyama M., Shiraishi J., Abe H., Li Q., Engelmann R., Sone S., MacMahon H., and Doi K.: Effect of CAD on radiologists’ responses in distinction between malignant and benign pulmonary nodules on high-resolution CT. Proc. SPIE Medical Imaging (SPIE MI) 5749: 502-507, February 2005.

  • 108

    Suzuki K., Shiraishi J., Li F., Abe H., MacMahon H., and Doi K.: Effect of massive training artificial neural networks for rib suppression on reduction of false positives in computerized detection of nodules on chest radiographs. Proc. SPIE Medical Imaging (SPIE MI) 5747: 97-103, February 2005.

  • 109

    Li F., Li Q., Aoyama M., Shiraishi J., Abe H., Suzuki K., Engelmann R., Sone S., MacMahon H., and Doi K.: Usefulness of computerized scheme for differentiating benign from malignant lung nodules on high-resolution CT. Computer Assisted Radiology and Surgery (CARS), pp. 946-951, June 2004.

  • 110

    Suzuki K. and Doi K.: Characteristics of a massive training artificial neural network in the distinction between lung nodules and vessels in CT images. Computer Assisted Radiology and Surgery (CARS), pp. 923-928, June 2004.

  • 111

    Suzuki K., Abe H., Li F., and Doi K.: Suppression of the contrast of ribs in chest radiographs by means of massive training artificial neural network. Proc. SPIE Medical Imaging (SPIE MI) 5370: 1109-1119, February 2004.

  • 112

    Suzuki K., Armato III S. G., Li F., Sone S., and Doi K.: Effect of a small number of training cases on the performance of massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose CT. Proc. SPIE Medical Imaging (SPIE MI) 5032: 1355-1366, February 2003.

  • 113

    Suzuki K., Horiba I., and Sugie N.: Simple unit-pruning with gain-changing training. Proc. IEEE Int. Workshop on Neural Networks for Signal Processing (NNSP) XI: 153-162, September 2001.

  • 114

    Ninagawa K., Umeyama T., Suzuki K., and Sugie N.: Voice separation in the frequency domain using image processing. Proc. Int. Conf. Software Engineering, Artificial Intelligence, Networking & Parallel/Distributed Computing (SNPD), pp. 746-753, August 2001.

  • 115

    Ninagawa K., Umeyama T., Suzuki K., and Sugie N.: Sound source separation in the frequency domain with image processing. Human-Computer Interaction (INTERACT), pp. 781-782, July 2001.

  • 116

    Suzuki K., Horiba I., and Sugie N.: Neural edge detector -a good mimic of conventional one yet robuster against noise-. Lecture Notes in Computer Science 2085: 303-310, June 2001.

  • 117

    Suzuki K., Horiba I., Sugie N., and Nanki M.: Computer-aided diagnosis system for coronary artery stenosis using a neural network. Proc. SPIE Medical Imaging (SPIE MI) 4322: 1771-1782, February 2001.

  • 118

    Suzuki K., Horiba I., Sugie N., and Nanki M.: Extraction of the contours of left ventricular cavity, according with those traced by medical doctors, from left ventriculograms using a neural edge detector. Proc. SPIE Medical Imaging (SPIE MI) 4322: 1284-1295, February 2001.

  • 119

    Suzuki K., Horiba I., and Sugie N.: Training under achievement quotient criterion. Proc. IEEE Int. Workshop on Neural Networks for Signal Processing (NNSP) X: 537-546, December 2000.

  • 120

    Suzuki K., Horiba I., and Sugie N.: Edge detection from noisy images using a neural edge detector. Proc. IEEE Int. Workshop on Neural Networks for Signal Processing (NNSP) X: 487-496, December 2000.

  • 121

    Suzuki K., Horiba I., and Sugie N.: Neural filter with selection of input features and its application to image quality improvement of medical image sequences. Proc. IEEE Int. Symp. Intelligent Signal Processing and Communication Systems (ISPACS) II: 783-788, November 2000.

  • 122

    Suzuki K., Horiba I., and Sugie N.: Signal-preserving training for neural networks for signal processing. Proc. IEEE Int. Symp. Intelligent Signal Processing and Communication Systems (ISPACS) I: 292-297, November 2000.

  • 123

    Suzuki K., Horiba I., and Sugie N.: Fast connected-component labeling based on sequential local operations in the course of forward raster scan followed by backward raster scan. Proc. Int. Conf. Pattern Recognition (ICPR) 2: 434-437, September 2000.

  • 124

    Suzuki K., Horiba I., and Sugie N.: An approach to synthesize filters with reduced structures using a neural network. Quantum Information 2: 205-218, March 2000.

  • 125

    Suzuki K., Horiba I., and Sugie N.: Efficient approximation of a neural filter for quantum noise removal in x-ray images. Proc. IEEE Int. Workshop on Neural Networks for Signal Processing (NNSP) IX: 370-379, August 1999.

  • 126

    Suzuki K., Horiba I., Sugie N., and Nanki M.: Noise reduction of medical x-ray image sequences using a neural filter with spatiotemporal inputs. Proc. Int. Symp. Noise Reduction for Imaging and Communication Systems (ISNIC), pp. 85-90, November 1998.

  • 127

    Suzuki K., Horiba I., Sugie N., and Nanki M.: A recurrent neural filter for reducing noise in medical x-ray image sequences. Proc. Int. Conf. Neural Information Processing (ICONIP) 1: 157-160, October 1998.

  • 128

    Suzuki K., Horiba I., and Sugie N.: Designing the optimal structure of a neural filter. Proc. IEEE Int. Workshop on Neural Networks for Signal Processing (NNSP) VIII: 323-332, September 1998.

  • 129

    Suzuki K., Horiba I., Sugie N., and Ikeda S.: Improvement of image quality of x-ray fluoroscopy using spatiotemporal neural filter which learns noise reduction, edge enhancement and motion compensation. Proc. Int. Conf. Signal Processing Applications and Technology (ICSPAT) 2: 1382-1386, October 1996.

  • 130

    Inaba T., He L., Murakami K., Suzuki K.: A study on temporal subtraction of chest radiographs using a genetic algorithm. Proc. Joint Conf. of Institutes of Electronics-Related Engineers p. P-059, 2008.

  • 131

    Ozawa Y., He L., Murakami K., Suzuki K.: A study on rib suppression in chest radiographs. Proc. Joint Conf. of Institutes of Electronics-Related Engineers p. P-065, 2008.

  • 132

    Arimura H., Katsuragawa S., Suzuki K., Li F., Shiraishi J., Doi K., and Sone S.: Development of a CAD scheme for lung nodule detection on CT images in lung cancer screening. Proc. 32nd Annual Meeting of Japanese Society of Radiological Technology, 2004.

  • 133

    Suzuki K., Horiba I., and Sugie N.: Edge detection from noisy images using a neural edge detector. Proc. 62nd Annual Meeting of Information Processing Society of Japan pp. 193-194, 2001.

  • 134

    Ninagawa K., Umeyama T., Suzuki K., and Sugie K.: Separation of sound sources in the spatial domain in sound spectrogram. Seminar of Institute of Electrical Engineers of Japan pp. 36-37, 2001.

  • 135

    Suzuki K., Horiba I., Sugie N., and Nanki M.: Contour extraction of the left ventricular cavity from digital subtraction angiograms using a neural edge detector. Technical Report of IEICE MI2000-35: 25-30, 2000.

  • 136

    Suzuki K., Horiba I., Sugie N., and Nanki M.: Contour extraction of the left ventricular cavity from digital subtraction angiograms using a neural edge detector. Technical Report of IEICE MI2000-35: 25-30, 2000.

  • 137

    Suzuki K., Horiba I., Sugie N., and Nanki M.: Contour extraction of left ventricles using a neural edge detector. Proc. Annual Meeting of Institute of Electronics, Information and Communication Engineers p. 368, 2000.

  • 138

    Suzuki K., Horiba I., and Sugie N.: Fast algorithm for labeling of connected components in binary images. Technical Report of IEICE PRMU99-123: 157-164, 1999.

  • 139

    Kurebayashi T., Uozumi E., Yoshida Y., Suzuki K., Horiba I., Okabayashi S., Yamamoto S., and Sugie N.: Effect of sounds on mind – analysis of the main theme of music -. Proc. Joint Conf. of Institutes of Electronics-Related Engineers p. 353, 1999.

  • 140

    Suzuki K., Horiba I., and Sugie N.: Realization of the approximate filter of a neural filter by analysis of its functions. Proc. Joint Conf. of Institutes of Electronics-Related Engineers p. 420, 1997.

  • 141

    Suzuki K., Hara N., Horiba I., Sugie N., and Ishikawa K.: A method for removing redundant units of supervised neural networks and its evaluation in an application to a neural filter. Technical Report of IEICE NC96-67: 71-78, 1996.

  • 142

    Teramoto A., Hara N., Horiba I., Sugie N., and Suzuki K.: A method for locally selecting filters using a neural network. Proc. 7th Annual Meeting of Japanese Neural Network Society pp. 127-128, 1996.

  • 143

    Suzuki K., Hara N., Horiba I., Sugie N., and Koike K.: A new method for optimizing the structure of a supervised neural network. Proc. 7th Annual Meeting of Japanese Neural Network Society pp. 106-107, 1996.

  • 144

    Teramoto A., Horiba I., Sugie N., Hara N., and Suzuki K.: Improvement of image quality by adaptive K-nearest neighbor averaging filter. Technical Report of IEICE IE96-41: 1-8, 1996.

  • 145

    Suzuki K., Hayashi T., Horiba I., Sugie N., and Koike K.: Improvement of image quality of x-ray fluoroscopy using a spatiotemporal neural filter which has learned noise reduction, edge enhancement and motion compensation. Technical Report of IEICE IE96-44: 25-32, 1996.

  • 146

    Suzuki K., Horiba I., and Sugie N.: Noise reduction of x-ray fluoroscopy using spatiotemporal neural filter. Technical Report of IEICE IE96-13: 37-44, 1996.

  • 147

    Suzuki K., Ikeda S., Suzuki K., and Imai N.: Development of an automated control system for x-ray filters. Proc. 52nd Annual Meeting of Japanese Society of Radiological Technology p. 80, 1996.

  • 148

    Hara N., Teramoto A., Suzuki K., Horiba I., and Sugie N.: A method for pruning units in neural networks. Proc. Joint Conf. of Institutes of Electronics-Related Engineers p. 302, 1995.

  • 149

    Nanki M., Kato M., Hori H., Haruta K., Horiba I., and Suzuki K.: Evaluation of a new subtraction technique in coronary DSA. Proc. 90th Annual Meeting of Japanese Circulation Society, 1993.

  • 150

    Suzuki K., Horiba I., Ikegaya K., and Nanki M.: A method for reducing artifacts in cardiac DSA images. Proc. Joint Conf. of Institutes of Electronics-Related Engineers, p. 351, 1992.

  • 151

    Suzuki K., Ueda K., Horiba I., and Ikegaya K.: A neural network model for predicting analog values. Proc. Joint Conf. of Institutes of Electronics-Related Engineers, p. 286, 1992.

  • 152

    Suzuki K., Ema H., Ueda K., Yamada M., Horiba I., and Ikegaya K.: Recognition of the parking occupancy status using a neural network. Proc. Joint Conf. of Institutes of Electronics-Related Engineers, p. 642, 1991.

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Invited / Review Articles in Journals / Magazines

ID

Title

  • 1

    Suzuki K.: Deep Learning in Medical Image Processing. JMP Magazine 152-Advanced Medicine NAVIGATOR: Frontiers of Medicine and AI: 8-10, February 2022 (Invited).

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  • 2

    Suzuki K.: AI-aided diagnosis of medical images with small-data deep learning. Precision Medicine 5(1): 90-92, January 2022 (Invited).

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  • 3

    Suzuki K.: AI-aided diagnosis of medical images with deep learning. BIO Clinica 36(7): 92-94, July 2021, (Invited).

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  • 4

    Suzuki K.: Small data deep learning and its applications to medical image processing and computer-aided diagnosis. Journal of Clinical and Experimental Medicine 274(9): 737-742, August 2020 (Invited).

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  • 5

    Suzuki K.: Medical image processing and diagnostic aid with deep learning. Precision Medicine 3(5): 87-91, May, 2020 (Invited).

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  • 6

    Suzuki K.: Medical Imaging Systems. JMAI Letter 2: 53-54, March, 2020 (Invited).

  • 7

    Suzuki K.: Recent Trends in Artificial Intelligence – Image Processing. Innervision 34(2): 35-37, February 2019 (Invited).

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  • 8

    Suzuki K.: AI-Aided Medical Image Diagnosis in CT Colonography. Innervision 34(1): 47-50, January 2019 (Invited).

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  • 9

    Suzuki K.: Cutting-edge Technology of Deep Learning and AI-Aided Medical Image Diagnosis in Diagnostic Imaging. Special Issue on Multislice CT 2018 Book, Medical 50(8): 40-50, July 2018 (Invited).

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  • 10

    Suzuki K.: Cutting-edge Technology in Image Processing and Recognition with Deep Learning. Innervision 33(7): 30-35, July 2018 (Invited). (ディープラーニングによる画像処理・認識技術の最前線,月刊インナービジョン2018年7月号)

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  • 11

    Suzuki K.: Deep Learning Applications, Research and Development in Medical Imaging: Introduction. Medical Imaging Technology 35(4): 177-179, September 2017 (Invited). (特集/医用画像工学分野におけるディープラーニング応用と研究開発 —序文—, Medical Imaging Technology 2017年9月号

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  • 12

    Suzuki K.: Survey of Deep Learning Applications to Medical Image Analysis. Medical Imaging Technology 35(4): 212-226, September 2017 (Invited, peer-reviewed).

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  • 13

    Suzuki K.: Overview of Deep Learning in Medical Imaging. Radiological Physics and Technology 10(3): 257-273, 2017 (Invited, peer-reviewed).

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  • 14

    Suzuki K.: Machine Learning in Medical Imaging Before and After Introduction of Deep Learning. Journal of Medical Imaging and Information Science 34(2): 14-24, 2017 (Invited, peer-reviewed).

  • 15

    Yì-Xiang J. Wang, Romaric Loffroy, Richa Arora, Kenji Suzuki, Chang-Hee Lee, Hsiao-Wen Chung, Edwin H. G. Oei, Gavin P. Winston, Chin K. Ng, Relative Income of Clinical Faculty members vs. Science Faculty Members in University settings – A short survey of France, Hong Kong, India, Japan, South Korea, The Netherlands, Taiwan, UK, and USA. Quant Imaging Med Surg 4(6): 500-501, 2014 (Invited).

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  • 16

    Suzuki K., Wang F., Shen D, Yan P.: Editorial, Machine Learning in Medical Imaging. International Journal of Biomedical Imaging 2012: Article ID 123727, 2 pages, 2012 (Invited).

  • 17

    He L., Chao Y., Suzuki K., and Nakamura T.: A high-speed labeling algorithm for three-dimensional binary images. ImageLab 8: 48-52, 2010 (Invited).

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  • 18

    He L., Chao Y., Suzuki K., Nakamura T., and Itoh H.: A fast two-scan connected-component labeling algorithm for binary images. ImageLab 9: 15-19, 2008 (Invited).

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  • 19

    Suzuki K.: Applications of computer-aided diagnosis by means of massive-training artificial neural networks (MTANN) to diagnosis of the thorax. Medical 39: 1202-1209, 2007 (Invited).

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  • 20

    Suzuki K.: Massive training artificial neural network (MTANN): A versatile pattern-recognition technique which learns abnormal and normal opacities for computer-aided diagnosis. Innervision 19: 31-37, 2004 (Invited).

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  • 21

    Suzuki K.: Recommended readings on radiological imaging research – On artificial neural networks. Japanese Journal of Radiological Technology 60: 1085-1100, 2004 (Invited).

  • 22

    Suzuki K.: Recommended readings on neural networks. Gazo Tsushin 26: 48-50, 2003 (Invited).

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  • 23

    Suzuki K. and Horiba I.: Recognition of artery stenosis using a neural network for predicting analogue values. ImageLab 6: 63-66, 1995 (Invited).

Books

ID

Title

Book Chapters

ID

Title

  • 1

    Jin Z., Yuan T., Tokuda Y., Naoi Y., Tomiyama N., and Suzuki K.: Radiomics: Approach to Precision Medicine, Artificial Intelligence and Machine Learning for Healthcare, Lim, Vaidya, Chen, T. Jain, and L. Jain Eds., Springer, pp. 17-29, 2022. (ISBN 978-3-031-11153-2) (invited)

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  • 2

    Suzuki K.: Computerized Detection of Lesions in Diagnostic Images with Early Deep Learning Models. Machine and Deep Learning in Radiation Oncology, Medical Physics and Radiology, Issam El Naqa. Ruijiang Li, Martin J. Murphy Eds., 2nd Edition, Springer-Nature (Berlin, Heidelberg), pp. 175-204, 2022. (ISBN 978-3-030-83046-5) (Invited)

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  • 3

    Suzuki K.: Deep Learning in Medical Image Processing and Computer-Aided Diagnosis, Biomedical Engineering, Miyauchi A. Miyahara Y. Eds., Jenny Stanford Publishing, pp. 297-318, November, 2021. (ISBN 9789814877633) (Invited)

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  • 4

    Suzuki K.: Massive-Training Artificial Neural Network (MTANN), Introduction to Deep Learning for Medical Images, Hiroshi Fujita Ed., Ohmsha, pp. 67-69, 2020. (ISBN 978-4-274-22544-4) (Invited)

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  • 5

    Zarshenas A. and Suzuki K.: Deep Learning for Medical Image Processing: Bones and Soft Tissue Separation in Chest Radiographs, Lung Cancer and Imaging, A. El-Baz, J. S. Suri Eds., IOP Publishing, December, 2019. (ISBN: 978-0-7503-2538-7) (invited)

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  • 6

    Tajbakhsh N. and Suzuki K.: A Comparative Study of Modern Machine Learning Approaches for Focal Lesion Detection and Classification in Medical Images: BoVW, CNN and MTANN, Artificial Intelligence in Decision Support Systems for Diagnosis in Medical Imaging, Suzuki K., Chen Y. Eds., Springer-Verlag (Germany), pp. 31-58, 2018. (ISBN 978-3-319-68843-5) (Invited)

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  • 7

    Zarshenas A. and Suzuki K.: Introduction to Binary Coordinate Ascent: New insights into efficient feature subset selection for machine learning, Artificial Intelligence in Decision Support Systems for Diagnosis in Medical Imaging, Suzuki K., Chen Y. Eds., Springer-Verlag (Germany), pp. 59-83, 2018. (ISBN 978-3-319-68843-5) (Invited)

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  • 8

    Xu J., Zarshenas A., Chen Y., and Suzuki K.: Massive-Training Support Vector Regression with Feature Selection in Application of Computer-aided Detection of Polyps in CT Colonography, Emerging Developments and Practices in Oncology, Issam El Naqa Ed., IGI Global (Hershey, PA), pp. 153-190, 2018. (ISBN 9781522530855) (Invited)

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  • 9

    Suzuki K.: Computer-aided detection of lung cancer. Image-based Computer-assisted Radiation Therapy, Springer-Nature (Berlin, Heidelberg), Hidetaka Arimura Ed., pp. 9-40, 2017. (ISBN 978-981-10-2943-1) (Invited)

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  • 10

    Suzuki K.: Computerized Detection of Lesions in Diagnostic Images. Machine Learning in Radiation Oncology, Issam El Naqa, Ruijiang Li, Martin J. Murphy Eds., Springer-Verlag (Berlin, Heidelberg), pp. 101-131, 2015. (ISBN 978-3-319-18304-6) (Invited)

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  • 11

    Suzuki K.: Pixel-based machine learning in computer-aided diagnosis for lung and colon cancer. Machine Learning in Healthcare Informatics, Dua S., Acharya U.R., Dua P. Eds., Springer-Verlag (Berlin, Heidelberg), pp. 81-112, 2014. (ISBN 978-3-642-40016-2) (Invited)

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  • 12

    Huynh H. T. Karademira I., Oto A., and Suzuki K.: Liver Volumetry in MRI by Using Fast Marching Algorithm Coupled with 3D Geodesic Active Contour Segmentation, Computational Intelligence in Biomedical Imaging, Suzuki K. Ed., Springer (New York, NY), pp. 141-158, 2014. (ISBN 978-1-4614-7244-5)

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  • 13

    Chen S. and Suzuki K.: Bone Suppression in Chest Radiographs by Means of Anatomically Specific Multiple Massive-Training ANNs Combined with Total Variation Minimization Smoothing and Consistency Processing. Computational Intelligence in Biomedical Imaging, Suzuki K. Ed., Springer (New York, NY), pp. 211-235, 2014. (ISBN 978-1-4614-7244-5)

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  • 14

    Suzuki K.: Classification of Lesions by Use of Massive-Training Artificial Neural Networks. Handbook of Medical Image Analysis, Fujita H., Ishida T., and Katsuragawa S. Eds., Ohm (Tokyo, Japan), 898 pp., 2012. (ISBN 978-4-274-21282-6) (Invited)

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  • 15

    Suzuki K.: Neural Networks. Handbook of Medical Image Analysis, Fujita H., Ishida T., and Katsuragawa S. Eds., Ohm (Tokyo, Japan), 898 pp., 2012. (ISBN 978-4-274-21282-6) (Invited)

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  • 16

    Chen S. and Suzuki K.: Computerized detection of lung nodules on chest radiographs: Application of bone suppression imaging by means of anatomical-segment-specific multiple massive-training ANNs, Machine Learning in Computer-Aided Diagnosis: Medical Imaging Intelligence and Analysis, Suzuki K. Ed., IGI Global (Hershey, PA), pp. 122-144, 2012. (ISBN 9781466600591)

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  • 17

    Xu J. and Suzuki K.: Computer-aided detection of polyps in CT colonography by means of feature selection and massive-training support vector regression, Machine Learning in Computer-Aided Diagnosis: Medical Imaging Intelligence and Analysis, Suzuki K. Ed., IGI Global (Hershey, PA), pp. 178-201, 2012. (ISBN 9781466600591)

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  • 18

    Suzuki K.: Computer-aided detection of lung nodules in chest radiographs and thoracic CT. Lung Imaging and Computer Aided Diagnosis, CRC press (Boca Raton, FL), pp. 297-318, 2011. (ISBN 978-1439845578) (Invited)

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  • 19

    Suzuki K.: Computerized segmentation of organs by means of geodesic active contour level-set algorithm. Multi Modality State of the Art in Image Segmentation and Registration, El-Baz A. Ed., Springer (New York, NY), pp. 103-128, 2011. (ISBN 978-1-4419-8194-3) (Invited)

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  • 20

    Suzuki K.: Massive-training artificial neural networks for supervised enhancement/suppression of lesions/patterns in medical images. Focus on Artificial Neural Networks, John A. Flores Ed., Nova Science Publishers (Hauppauge, NY), pp. 129-150, 2011. (ISBN 978-1-61324-285-8) (Invited)

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  • 21

    Suzuki K.: Pixel-based artificial neural networks in computer-aided diagnosis. Artificial Neural Networks – Methodological Advances and Biomedical Applications, K. Suzuki Ed., In-Tech (Vukovar, Croatia), pp. 71-92, 2011. (ISBN 978-953-307-243-2) (Invited)

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  • 22

    Suzuki K. and Dachman A. H.: Computer-aided diagnosis in CT colonography. Atlas of Virtual Colonoscopy, 2nd Edition, Dachman A. H. and Laghi A. Eds., Springer (New York, NY), pp. 163-182, 2011. (ISBN 978-1-4419-5851-8) (Invited)

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  • 23

    Suzuki K. and Dachman A. H.: Usefulness of computer-aided diagnosis in CT colonography. Colonoscopia virtual, Patricia Carrascosa, Carlos Capunay, and Jorge A. Soto Eds., Liberia Akadia Editorial (Buenos Aires, Argentina), pp. 73-88, 2011. (ISBN 978-987-570-147-2) (Invited)

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  • 24

    Epstein M. L., Sheu I., Suzuki K.: Hessian matrix-based shape extraction and volume growing for 3D polyp segmentation in CT colonography. Pattern Recognition-Recent Advances, Adam Herout Ed., In-Tech (Vukovar, Croatia), pp. 405-418, 2010. (ISBN 978-953-7619-90-9) (Invited)

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  • 25

    Suzuki K.: Massive-training artificial neural networks (MTANN) in computer-aided detection of colorectal polyps and lung nodules in CT. Machine Learning, Yagang Zhang Ed., In-Tech (Vukovar, Croatia), pp. 343-366, 2010. (ISBN 978-953-307-033-9) (Invited)

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  • 26

    Giger M. L. and Suzuki K.: Computer-aided diagnosis (CAD). Biomedical Information Technology, David Dagan Feng Ed., Academic Press, pp. 359-374, 2007. (ISBN 978-0-12-373583-6)

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  • 27

    Suzuki K.: Focus, motion, deblurring, smoothing, edge-preserving smoothing, and image restoration. Dictionary of Cognitive Science, Japanese Cognitive Science Society Ed., 1,032 pp., Kyoritsu Shuppan, Tokyo, Japan, 2002. (ISBN 978-4320094451) (Invited)

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Invited Talks / Keynote Speaker

ID

Title

Grants

ID

Title

Kenji Suzuki Laboratory

Institute of Innovative Research (IIR)
Tokyo Institute of Technology

Biomedical AI Research Unit