Biography:

Kenji Suzuki, Ph.D, has been actively researching deep learning in medical imaging and AI-aided diagnosis for over 25 years. Prior faculty experiences include University of Chicago and Illinois Institute of Technology. He has published 14 books and over 340 papers and is an inventor on a dozen of licensed and commercialized patents, including one of the earliest deep learning patents. He has been awarded numerous grants, including grants from NIH, NEDO, and JST, chaired 98 international conferences, and served as editor of over 40 leading international journals. Dr. Suzuki has been Professor in Institute of Innovative Research at Tokyo Institute of Technology.

Speech title: AI Doctor and Smart Medical Imaging with Deep Learning

Abstract:

It is said that artificial intelligence (AI) 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, robotics, and imaging fields, because “learning from examples or data” is crucial to handling a large amount of data (“big data”) coming from vision and imaging systems. Deep learning is a versatile, powerful framework that can acquire image-processing and recognition 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 25 years. In this talk, AI-aided diagnosis and smart medical imaging with deep learning are introduced, including 1) computer-aided diagnosis for lung cancer and colon cancer in CT, and 2) AI imaging for a) separation of bones from soft tissue in chest radiographs and b) radiation dose reduction in CT and mammography.