Kenji Suzuki

Kenji Suzuki received his B.S. and M.S. degrees in engineering from Meijo University, Japan, in 1991 and 1993, respectively, and his Ph.D. degree (by Published Work) in engineering from Nagoya University, Japan, in 2001. From 1993 to 2001, he worked at Hitachi Medical Corporation, and then Aichi Prefectural University as faculty. In 2001, he joined Department of Radiology at The University of Chicago, as Research Associate. Since 2006, he has been Assistant Professor of Radiology, Medical Physics, and Cancer Research Center there. Dr. Suzuki’ research interests include computer-aided diagnosis and machine learning. He has published more than 190 papers (including 70 peer-reviewed journal papers), 4 books, and 13 book chapters, and edited 4 journal special issues. He has an h-index of 22 as of 2011. He was awarded more than 25 grants including NIH R01 grants. He has been serving as the Editor-in-Chief and an Associate Editor of 13 leading international journals, including Medical Physics, Academic Radiology, and Algorithms. He has been serving as a referee for more than 45 international journals, an organizer of 5 international conferences, and a program committee member of 50 international conferences. He had supervised/co-supervised more than 60 graduate/undergraduate students, postdocs/computer scientists, and visiting professors. He has received numerous awards, including a University of Chicago Paul C. Hodges Award, three Certificate of Merit Awards and Research Trainee Prize from RSNA, Young Investigator Award from Cancer Research Foundation, an IEEE Outstanding Member Award, and Honorable Mention Poster Award at SPIE International Symposium on Medical Imaging. He has been a Senior Member of IEEE since 2004.

Publications

Machine Learning in Computer-Aided Diagnosis: Medical Imaging Intelligence and Analysis
Kenji Suzuki. © 2012. 524 pages.
Medical imaging is an indispensable tool for modern healthcare. Machine leaning plays an essential role in the medical imaging field, with applications including...
Computerized Detection of Lung Nodules on Chest Radiographs: Application of Bone Suppression Imaging by Means of Multiple Massive-Training ANNs
Sheng Chen, Kenji Suzuki. © 2012. 23 pages.
Most lung nodules missed by radiologists as well as Computer-Aided Diagnostic (CADe) schemes overlap ribs or clavicles in Chest Radiographs (CXRs). This chapter...