Kenji Suzuki

Profile
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, Univ. of Chicago
2014- Associate Professor, Dept. of Electrical and Computer Engineering, 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, medical imaging, artificial intelligence

Laboratory for Future Interdisciplinary Research of Science and Technology(IIR, Tokyo Tech)
Illinois Institute of Technology(U.S.A)

 http://www.ece.iit.edu/~ksuzuki/
 ksuzuki@iit.edu

Research Hub Group:Information and Artificial Intelligence Research International Hub Group

Research Highlights

  • Development and commercialization of machine learning models that learn images directly (1994)
  • Development of machine learning for separating bone from soft tissue in chest radiographs (2004)

Press

  • 02/2016
    Dr. Suzuki’s comments were included in the article “IBM’s Automated Radiologist Can Read Images and Medical Records” in MIT Technology Review.
  • 04/2014
    Dr. Suzuki’s study was featured in an article “Adding Structure: How to Boost Reporting Efficiency” on HealthImaging.com.
  • 03/2013
    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.
  • 01/2012
    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.

Selected Awards

  • 2003, 2006, 2009
    Certificate of Merit Award from RSNA
  • 2010
    IEEE Outstanding Member Award from IEEE Chicago Section
  • 2011
    Kurt Rossmann Award for Excellence in Teaching from University of Chicago
  • 2014
    The 2014 Best Paper Award from IEICE
  • 2016
    The Most Cited Paper Award from EANM and Springer-Nature

Selected Publications

  • 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).