2001 Research Associate, Kurt Rossmann Laboratories for Radiologic Image Research, Univ. of Chicago
2004 Research Associate (Assistant Professor), Dept. of Radiology, Univ. of Chicago
2006 Assistant Professor, Dept. of Radiology & Graduate Program in Medical Physics & Comprehensive Cancer Center, Univ. of Chicago
2014- Associate Professor, Dept. of Electrical and Computer Engineering & Medical Imaging Research Center, Illinois Institute of Technology
2017- Professor (Specially Appointed), Institute of Innovative Research, Tokyo Institute of Technology
Field of Specialization
Machine learning/deep learning/computer-aided diagnosis/artificial intelligence/medical imaging
Laboratory for Future Interdisciplinary Research of Science and Technology（IIR, Tokyo Tech）
Illinois Institute of Technology（U.S.A）
Research Hub Group：Information and Artificial Intelligence Research International Hub Group
- 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 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.
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’s comments were included in the article “IBM’s Automated Radiologist Can Read Images and Medical Records” in MIT Technology Review.
Dr. Suzuki’s study was featured in an article “Adding Structure: How to Boost Reporting Efficiency” on HealthImaging.com.
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 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.
- 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.