Speaker

Prof. Wanquan Liu

IEEE Senior Member

(Keynote Speaker)

Sun Yat-sen University, China

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Speech Title:Investigations on Supervised Learning and Weakly Semi-Supervised Learning with Applications on Medical Image Segmentations

Abstract: Medical image segmentation is a fundamental problem in computer aided diagnosis, whose performance heavily relies on large amounts of high quality pixel level annotations that are costly and difficult to obtain in clinical practice. To address this challenge, this work systematically investigates supervised learning and weakly semi supervised learning methodologies for medical image segmentation. Under the fully supervised setting, we focus on the complex appearance, ambiguous boundaries, and large inter domain variations commonly observed in endoscopic images, and propose a label consistent augmentation framework that integrates generative modeling with strict pixel level supervision, enabling effective expansion of appearance diversity while preserving geometric and semantic consistency. This design significantly improves generalization across multi center and multi device endoscopic datasets. Furthermore, to reduce annotation dependency, we study weakly semi supervised segmentation with point level supervision and introduce a point neighborhood learning paradigm that explicitly exploits reliable local structures around sparse annotations, transforming extremely weak supervision into effective training signals. By combining neighborhood based supervision, pseudo label quality control, and a teacher student framework, the proposed approach mitigates noise and bias inherent in weak supervision. Extensive experiments conducted on multiple endoscopic datasets, including nasal and gastrointestinal endoscopy, demonstrate consistent performance gains under different supervision regimes, highlighting the robustness and cross dataset generalizability of the proposed methods and their potential for practical clinical deployment.

Brief Bio: Dr Wanquan Liu (Senior Member, IEEE): He is a Professor in the School of Intelligent Systems Engineering at Sun Yat-sen University. He received the B.Sc. degree in Applied Mathematics from Qufu Normal University, P.R. China, in 1985; the M.Sc. degree in Control Theory and Operation Research from Chinese Academy of Sciences in 1988, and the Ph.D. degree in Electrical Engineering from Shanghai Jiaotong University, Shanghai, P.R. China, in 1993. He once held the ARC Fellowship from Australian Research Council, U2000 Fellowship from the University of Sydney and JSPS Fellowship from Japan and attracted research funds from different resources. He has published over 400 papers in reputed journals and international conferences with more than 8000 citations. He is the Editor-in-Chief for the journal Mathematical Foundations of Computing and serves on the editorial board of several international journals. His research interests include machine learning, intelligent control, and smart home for the aged care. <Personal Webpage>