BMAI
Biomedical Artificial
Intelligence Research Unit
Biomedical Artificial
Intelligence Research Unit
En
AI-aided Diagnosis
Cancer Research (CR)
We developed a technique that uses a multiple massive-training artificial neural network scheme (multi-MTANN) to reduce false positives (FPs) in a computer-aided diagnostic (CAD) scheme for nodule detection on chest radiographs. Our database consisted of 91 solitary pulmonary nodules including 64 malignant nodules and 27 benign nodules in 91 chest radiographs. With our CAD scheme based on a dif Read more...
Low-dose helical CT (LDCT) is being applied as a modality for lung cancer screening. It may be difficult, however, for radiologists to distinguish malignant from benign nodules in LDCT. Our purpose in this study was to develop a computer-aided diagnostic (CAD) scheme for distinction between benign and malignant nodules in LDCT by use of a massive-training artificial neural network (MTANN). The Read more...
A CAD Utilizing 3D Massive-Training ANNs for Detection of Flat Lesions in CT Colonography in a Large Multicenter Clinical Trial
We developed a computer-aided diagnostic (CAD) scheme for detection of flat lesions (also called flat polyps or depressed polyps) in CT colonography (CTC) in a large multicenter clinical trial in collaboration with Don C. Rockey, M.D., at the Southwest Medical C Read more...
– A CAD Utilizing 3D Massive-Training ANNs for Detection of Flat Lesions in CT Colonography in a Large Multicenter Clinical Trial
– Polyp Detection in CT Colonography: Performance of a CAD Scheme Incorporating 3D MTANNs on False-Negative Polyps in a Multicenter Clinical Trial
– Ensemble Training for a Mixture of Expert 3D MTANNs for Eliminating Multiple False-Positive Source Read more...
– Automated CT Liver Volumetry by Use of Three-Dimensional Fast-Marching and Level-Set Segmentation
– Reduction of Quantum Noise in Low-Dose Double-Contrast Radiographs of the Stomach
– Enhanced Digital Chest Radiography: Temporal Subtraction Combined with Virtual Dual-EnergyETechnology for Improved Conspicuity of Growing Cancers and Other Pathologic Changes
– Virtu Read more...
– A CAD Utilizing 3D Massive-Training ANNs for Detection of Flat Lesions in CT Colonography in a Large Multicenter Clinical Trial
– Polyp Detection in CT Colonography: Performance of a CAD Scheme Incorporating 3D MTANNs on False-Negative Polyps in a Multicenter Clinical Trial
– Ensemble Training for a Mixture of Expert 3D MTANNs for Eliminating Multiple False-Positive Source Read more...
– Automated CT Liver Volumetry by Use of Three-Dimensional Fast-Marching and Level-Set Segmentation
– Reduction of Quantum Noise in Low-Dose Double-Contrast Radiographs of the Stomach
– Enhanced Digital Chest Radiography: Temporal Subtraction Combined with Virtual Dual-EnergyETechnology for Improved Conspicuity of Growing Cancers and Other Pathologic Changes
– Virtu Read more...
– A CAD Utilizing 3D Massive-Training ANNs for Detection of Flat Lesions in CT Colonography in a Large Multicenter Clinical Trial
– Polyp Detection in CT Colonography: Performance of a CAD Scheme Incorporating 3D MTANNs on False-Negative Polyps in a Multicenter Clinical Trial
– Ensemble Training for a Mixture of Expert 3D MTANNs for Eliminating Multiple False-Positive Source Read more...
– Neural Edge Enhancer for Supervised Edge Enhancement from Noisy Images
– Analysis of the Neural Edge Enhancer Trained for Edge Enhancement in Noisy Images
– Reduction of Noise from Images by Use of a Neural Filter
– A Method for Designing the Optimal Structure of a Neural Filter
– Efficient Approximation of Neural Filters for Removing Read more...