Lecturer Profile |
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). Since 2017, he has been jointly appointed in Institute of Innovative Research (IIR), Tokyo Institute of Technology, Japan, as Full Professor. He published 320 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 46. 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 Translation Medicine (IF: 16.8), an organizer of 62 international conferences, and a program committee member of 170 international conferences. He gave 110 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. |
Lecture Abstract |
It is said that artificial intelligence driven by deep learning would make the 4th Industrial Revolution. Deep leaning becomes one of the most active areas of research in computer vision, pattern recognition, and imaging fields, because “learning from examples or data” is crucial to handling a large amount of data (“big data”) coming from informatics and imaging systems. Deep learning is a versatile, powerful framework that can acquire image-processing and analysis functions through training with image examples; and it is an end-to-end machine-learning model that enables a direct mapping from raw input data to desired outputs, eliminating the need for handcrafted features in conventional feature-based machine learning. I invented ones of the earliest deep-learning models for image processing, semantic segmentation, object enhancement, and classification of patterns in medical imaging. I have been actively studying on deep learning in medical imaging in the past 23 years. In this talk, AI-aided diagnosis and smart medical imaging with deep learning are introduced, including 1) computer-aided diagnosis for lung cancer in CT, 2) distinction between benign and malignant lung nodules in CT, 3) polyp detection and classification in CT colonography, 4) separation of bones from soft tissue in chest radiographs, and 5) radiation dose reduction in CT and mammography. |
