报告题目：Not-so-supervised Deep Learning for Medical Image Analysis
报告摘要
Artificial intelligence, especially deep learning with large-scale annotated datasets, has dramatically advanced the recognition performance in many domains including speech recognition, visual recognition and natural language processing. Despite its breakthroughs in above domains, its application to medical image analysis remains yet to be further explored, where large-scale fully and high-quality annotated datasets are not easily accessible. In this talk, I will share our recent progress on developing not-so-supervised deep learning methods, i.e., label-efficient, by leveraging an abundance of weakly-labeled and/or unlabeled datasets for medical image analysis, with versatile applications to disease diagnosis, lesion detection and segmentation, etc.
嘉宾简介
Prof. Hao Chen is an Assistant Professor at the Department of Computer Science and Engineering, Hong Kong University of Science and Technology (HKUST). He leads the SMART Lab and serves as Associate Director in Center of Medical Imaging and Analysis. He received the Ph.D. degree from The Chinese University of Hong Kong (CUHK) in 2017. He was a postdoctoral research fellow in CUHK and a visiting scholar in Utrecht University Medical Center previously. He has published 100+ papers (Google Scholar Citations 13K+, h-index 51) in MICCAI, IEEE-TMI, MIA, CVPR, AAAI, Radiology, Lancet Digital Health, Nature Machine Intelligence, etc. He also has rich industrial research experience (e.g., Siemens and startup), and holds a dozen of patents in AI and medical image analysis. He received several premium awards such as MICCAI Young Scientist Impact Award in 2019. He serves as the Associate Editor of Frontiers in Artificial Intelligence and Frontiers in Big Data. He served as the Program Committee of multiple international conferences including Area Chair of MICCAI 2021-2022, ISBI 2022, MIDL 2022 and SPC of AAAI 2022, etc. He also led the team winning 15+ medical grand challenges.
特别感谢本次Webinar主要组织者：
胡凯（湘潭大学）