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X-WR-CALNAME:Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系
X-ORIGINAL-URL:https://ece.hku.hk
X-WR-CALDESC:Events for Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系
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TZID:Asia/Hong_Kong
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DTSTART:20240101T000000
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DTSTART;TZID=Asia/Hong_Kong:20250512T140000
DTEND;TZID=Asia/Hong_Kong:20250512T150000
DTSTAMP:20260509T183744
CREATED:20250603T035657Z
LAST-MODIFIED:20250603T035719Z
UID:111583-1747058400-1747062000@ece.hku.hk
SUMMARY:Towards Federated and Annotation-efficient Deep Learning for Medical Image Analysis
DESCRIPTION:Zoom Link : https://hku.zoom.us/j/91765972342?pwd=CHXoKEknnfPc6zbhHCADi7A1abVUyI.1 \nAbstract\nAs deep learning is increasingly applied in medical image analysis\, developing efficient and accurate models has become crucial. However\, traditional deep learning methods usually require large amounts of annotated data\, posing a significant challenge in medical imaging due to complex data collection and high annotation costs. Furthermore\, privacy and security concerns restrict data sharing and collaboration between institutions.Federated learning (FL) and annotation-efficient techniques have emerged to address these issues. This seminar explores how combining federated learning with annotation-efficient methods can advance intelligent medical image analysis. The key topics include: 1. Annotation-efficient strategies: Discussing self-supervised and weakly-supervised learning methods to enhance training efficiency and model performance when labeled data is limited; 2. Federated learning applications: Exploring how federated learning enables distributed model training across multiple institutions without data sharing\, thereby protecting data privacy; 3. Practical applications and challenges: Analyzing specific scenarios such as disease diagnosis and organ segmentation\, discussing the strengths and limitations of federated learning and annotation-efficient techniques\, and forecasting future developments. \nSpeaker\nMr. LIN Li\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nSpeaker’s Biography\nLi LIN received his B.S. in Telecommunication Engineering from South China Normal University in 2018 and the M.S. in Information and Communication Engineering from Sun Yat-sen University in 2021. He is currently pursuing the Ph.D. degree in the Department of Electrical and Electronic Engineering at the University of Hong Kong\, Hong Kong. \nAll are welcome!
URL:https://ece.hku.hk/events/20250512-1/
LOCATION:Online via Zoom
CATEGORIES:Highlights,Seminar
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