Dr. Suzuki’s comments were included in the article “CT Colonography’s Slow Progress” in Radiology Today: 14 (3), p. 26. Read more...

Dr. Suzuki’s RSNA presentation, entitled “Observer Performance Study: Effect of Computer-Aided Diagnosis (CADx) on the Performance of Radiologists in Distinguishing Neoplastic from Non-Neoplastic Lesions in CT Colonography (CTC)” by Suzuki K., Iinuma G., Miyake M, Shimada N, Hock D, and Dachman A Read more...

Dr. Suzuki’s research on pixel-based machine learning in medical images was featured in the article “Imaging Software That Learns By Example” on Science Life. Read more...

Dr. Suzuki’s research on machine learning in medical image analysis was featured in the article “Better Medicine Through Machine Learning” on Communications of the ACM (IF: 4.7): 55, pp. 17-19. Read more...

Dr. Suzuki’s study was featured in the article “Software automates CT liver volume measurements” on AuntMinnie.com. Read more...

Dr. Suzuki’s comments and his research were included in the article “A Search Engine for the Human Body” in MIT Technology Review.

Dr. Suzuki’s comments and his research were included in the article “Microsoft develops search engine for the human body” on TechEye. < Read more...

Our RSNA presentation entitled “Observer Performance Study: Effect of Computer-aided Detection (CADe) on the Performance of Expert Radiologists in Detection of “Difficult” Polyps in CT Colonography (CTC) in a Multicenter Clinical Trial” by Suzuki K., Hori M., Iinuma G., Friedman A. C., and Dachma Read more...

Dr. Suzuki and his research on his massive-training artificial neuralnetwork, entitled “The AI Doctor Is Ready To See You” in SingularityHub. Read more...

Dr. Suzuki and his research on pixel/voxel-based machine learning, was included in the article “Pixel-voxel based machine learning algorithm — Developments in medical image processing and analysis” in Medical and Diagnostic Imaging Technology Alert from Frost & Sullivan. Read more...

Report on RSNA Certificate of Merit Award for our exhibit, entitled “Can CAD help improve the performance of radiologists in detection of “difficult” polyps in CT colonography?” by Suzuki K., et al. Innervision 25: 56-57, 2010. Read more...