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Prof. Kenji Suzuki
Institute of Science Tokyo, Japan
Kenji Suzuki, Ph.D. worked at Hitachi Medical Corp, Aichi Prefectural University, Japan, as a faculty member, in Department of Radiology, University of Chicago, as Assistant Professor, and Medical Imaging Research Center, Illinois Institute of Technology, as Associate Professor (Tenured). He is currently a Full Professor (Tenured) & Founding Director of Biomedical Artificial Intelligence Research Unit, Institute of Integrated Research, Institute of Science Tokyo, Japan. He published more than 395 papers (including 125 peer-reviewed journal papers). He has been actively researching on deep learning in medical imaging and AI-aided diagnosis in the past 25 years, especially his early deep-learning model was proposed in 1994. His papers were cited more than 16,000 times, and his h-index is 63. He is inventor on 37 patents (including ones of earliest deep-learning patents), which were licensed to several companies and commercialized via FDA approvals. He published 16 books and edited 20 journal special issues. He has been awarded numerous grants including NIH, NEDO, and JST grants, totaling $8M. He serves as Editors of more than 20 leading international journals including Pattern Recognition and AI. He chaired 110 international conferences. He is a Fellow of IARIA. He received 25 awards, including 3 Best Paper Awards in leading journals.
Speech Title: "Small-Data Deep Learning for Diagnosis of Lesions and Medical AI Imaging"
Abstract: Deep learning has shown to be a breakthrough technology in many fields including medicine. The performance of deep learning increases as the amount of data increases and reaches at the human performance. My group has been actively studying on deep learning in medical imaging in the past 25 years, including ones of the earliest deep-learning models for medical image processing, lesion/organ segmentation, and classification of lesions in medical imaging. In this talk, "small-data" deep learning that does not require "big data", but can be trained with a small number of cases is introduced. Our small-data AI was applied to develop AI-aided diagnostic systems (ˇ°AI doctorˇ±) and deep-learning-based imaging for diagnosis (ˇ°virtual AI imagingˇ±), including 1) AI systems for detection and diagnosis of lung, colon, breast, and liver cancer in medical images, and 2) virtual AI imaging systems for separation of bones from soft tissue in chest radiographs and those for radiation dose reduction in CT, tomosynthesis, and mammography. Some of them have been commercialized via FDA approval in the U.S., including the first FDA-approved deep-learning product. Our small-data deep-learning technology would be useful for the development of AI in ˇ°small-dataˇ± areas where ˇ°big dataˇ± are not available.
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Prof. Yen-Wei Chen
Ritsumeikan University, Japan
Yen-Wei Chen received the B.E. degree in 1985 from Kobe Univ., Kobe, Japan, the M.E. degree in 1987, and the D.E. degree in 1990, both from Osaka Univ., Osaka, Japan. He was a research fellow with the Institute for Laser Technology, Osaka, from 1991 to 1994. From Oct. 1994 to Mar. 2004, he was an associate Professor and a professor with the Department of Electrical and Electronic Engineering, Univ. of the Ryukyus, Okinawa, Japan. He is currently a professor with the college of Information Science and Engineering, Ritsumeikan University, Japan. He is the founder and the first director of Center of Advanced ICT for Medicine and Healthcare, Ritsumeikan University, Japan. His research interests include medical image analysis, computer vision and computational intelligence. He has published more than 300 research papers in a number of leading journals and leading conferences including IEEE Trans. Image Processing, IEEE Trans. Medical Imaging, CVPR, ICCV, MICCAI. He has received many distinguished awards including ICPR2012 Best Scientific Paper Award, 2014 JAMIT Best Paper Award. He is/was a leader of numerous national and industrial research projects.
Speech Title: "Knowledge-Guided Deep Learning for Enhanced Medical Image Analysis"
Abstract: Recently, Deep Learning (DL) has played an important role in various academic and industrial domains, especially in computer vision and image recognition. Although deep learning (DL) has been successfully applied to medical image analysis, achieving state-of-the-art performance, few DL applications have been successfully implemented in real clinical settings. The primary reason for this is that the specific knowledge and prior information of human anatomy possessed by doctors is not utilized or incorporated into DL applications. In this keynote address, I will present our recent advancements in knowledge-guided deep learning for enhanced medical image analysis. This will include two research topics: (1) our proposed deep atlas prior, which incorporates medical knowledge into DL models; (2) language-guided medical image segmentation, which incorporates the specific knowledge of doctors as an additional language modality into DL models.
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Prof. Fengchun Tian
Chongqing University, China
Prof. Fengchun Tian received his B.Sc., M.Sc., and Ph.D. degrees in radio engineering, biomedical instruments and engineering, theoretical electric engineering from Chongqing University, Chongqing, P.R. China, in 1984, 1986, and 1996, respectively. Since 1984, he has been working in Chongqing University as a teacher. Since 2001, he has been a professor at Chongqing University. From 2007 to 2016, he was an adjunct professor at the University of Guelph, Canada. From 2015 to 2023, he was the director of Key Laboratory of Chongqing for Bio-perception and Intelligent Information Processing. From 2022 to 2024, he was the China Chair of International Society for Olfaction and Chemical Sensing (ISOCS). Since 2019, he has been the chair of academic degrees committee, School of Electronics and Communication Engineering, Chongqing University. Since 2020, he has been a member of working group on the IEEE P2520.1 standard. His research interests are focused on artificial olfaction (electronic nose) and biomedical and modern signal processing technology.
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Prof. Donghyun Kim(SPIE Fellow)
Yonsei University, Korea
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