报告题目：Reinforcement learning in Medical Image Analysis
会议ID：353 843 814
Reinforcement learning has achieved tremendous success in recent years, notably in complex games such as Atari, Go, and chess. However, researchers face multiple challenges when apply it to the medical image analysis. This talk will share our experiences and success in developing deep reinforcement learning to solve the current challenges in medical image analysis.
Dr. Shuo Li is a pioneer in conducting multi-disciplinary research for image centered medical data analytics to enable artificial intelligence (AI) in healthcare. He is a machine learning researcher by training, with degrees in software engineering and computer science (2006). Upon beginning his career at GE Healthcare (2006-2015), he earned a placement in working side-by-side with physicians in the hospital. As such, Dr. Li has a rich and unique background that incorporates science, engineering, and healthcare.
His current research focuses on the development of AI systems to solve the most challenging clinical and fundamental image centered data analytics problems in radiology, urology, surgery, rehabilitation, and cancer, with an emphasis on the innovations of learning schemes (regression learning, deep learning, reinforcement learning, sparse learning, spectral learning and manifold learning).
Dr. Li is a committee member in multiple highly influential conferences and societies. He is most notable for serving on the prestigious board of directors in the MICCAI society (2015-2023), where he is also the general chair for the MICCAI 2022 conference, a premier conference in medical imaging. He has over 200 publications, acted as the editor for six Springer books, and serves as an associate editor for several prestigious journals in the field. Throughout his career, he has received several awards from GE, various institutes and international organizations.