Kenji Suzuki


Kenji Suzuki

Specially Appointed Professor

Machine learningDeep learningComputer-aided diagnosisArtificial intelligenceMedical imaging


Kenji Suzuki, Ph.D. (by Published Work; Nagoya University) worked at Hitachi Medical Corp., Japan, Aichi Prefectural University, Japan, as a faculty member, and in Department of Radiology, University of Chicago, as Assistant Professor. In 2014, he joined Department of Electric and Computer Engineering and Medical Imaging Research Center, Illinois Institute of Technology, as Associate Professor (Tenured). In 2017, he was jointly appointed in World Research Hub Initiative (WRHI), Institute of Innovative Research (IIR), Tokyo Institute of Technology, Japan, as Specially Appointed Professor (equivalent to Visiting Professor). He published 330 papers (including 110 peer-reviewed journal papers). He has been actively studying deep learning in medical imaging and computer-aided diagnosis in the past 25 years. His papers were cited more than 13,000 times, and his h-index is 47. He is inventor on 30 patents (including ones of earliest deep-learning patents), which were licensed to several companies and commercialized. He published 11 books and 22 book chapters, and edited 13 journal special issues. He was awarded a number of grants as PI including NIH R01 and ACS. He served as the Editor of a number of leading international journals, including Pattern Recognition and Medical Physics. He served as a referee for 91 international journals such as Science Translational Medicine (IF: 16.8) and Nature Communications (IF: 12.4), an organizer of 62 international conferences, and a program committee member of 170 international conferences. He gave 120 invited talks and keynote speeches at international conferences. He received 26 awards, including Springer-Nature EANM Most Cited Journal Paper Award 2016 and 2017 Albert Nelson Marquis Lifetime Achievement Award.

Research Projects

  • Development and commercialization of early deep learning models that learn images directly (1994)

  • Development of machine learning for separating bone from soft tissue in chest radiographs (2004)


  • Dr. Suzuki’s research was featured in an article “Better Medicine Through Machine Learning” in the News section in Communications of the ACM, vol. 55, no.1, pp. 17-19, 2012.

  • Dr. Suzuki’s comments were included in an article “CT Colonography’s Slow Progress” in Radiology Today, vol. 14, no. 3, p. 26, March 2013.

  • Dr. Suzuki’s study was featured in an article “Adding Structure: How to Boost Reporting Efficiency” on

  • Dr. Suzuki’s comments were included in the article “IBM’s Automated Radiologist Can Read Images and Medical Records” in MIT Technology Review.

  • Dr. Suzuki’s comments as a deep learning pioneer were included in the the article “Deep learning poised to revolutionise diagnostic imaging” in one of the most prestigious journals in medicine, The Lancet Respiratory Medicine (Impact factor: 19.287).

  • Dr. Suzuki was awarded the prestigious “The 2017 Albert Nelson Marquis Lifetime Achievement Award” by Marquis Who’s Who for his professional dedication and career longevity in outstanding contributions with notable publications.


Research Associate, Kurt Rossmann Laboratories for Radiologic Image Research, Univ. of Chicago


Research Associate (Assistant Professor), Dept. of Radiology, Univ. of Chicago


Assistant Professor, Dept. of Radiology & Graduate Program in Medical Physics & Comprehensive Cancer Center, Univ. of Chicago


Associate Professor, Dept. of Electrical and Computer Engineering & Medical Imaging Research Center, Illinois Institute of Technology


Professor (Specially Appointed), Institute of Innovative Research, Tokyo Institute of Technology

2003, 2006, 2009

Certificate of Merit Award from RSNA


 IEEE Outstanding Member Award from IEEE Chicago Section


Kurt Rossmann Award for Excellence in Teaching from University of Chicago


The 2014 Best Paper Award from IEICE


The Most Cited Paper Award from EANM and Springer-Nature


Albert Nelson Marquis Lifetime Achievement Award, Marquis Who’s Who


Suzuki K, et al.: Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose CT. Medical Physics 30: 1602-1617, 2003.

Suzuki K, et al.: Neural edge enhancer for supervised edge enhancement from noisy images. IEEE Trans Pattern Analysis and Machine Intelligence 25: 1582-1596, 2003.


Suzuki K, et al.: Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN). IEEE Trans Medical Imaging 25: 406-416, 2006.


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


Nima Tajbakhsh and Suzuki K.: Comparing Two Classes of End-to-End Learning Machines for Lung Nodule Detection and Classification: MTANNs vs. CNNs. Pattern Recognition 63: 476–486, 2017.