26.05 11:00 - 12:00
USI East Campus, Room D0.10 |
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Abstract: We present new deep learning (DL) approaches for medical image analysis tasks (sparse annotation, segmentation, classification, denoising, etc). We show that it is often not enough to simply apply DL methods alone to tackle medical image analysis problems. Hence, our approaches are based on combinations of DL methods and algorithmic techniques (e.g., topological data analysis). For example, our sparse annotation schemes judiciously select the most representative samples to label. Actually, the problem of finding an optimal subset of samples (as sparse labeled data) to cover or represent an entire image dataset is an NP-hard problem, which can be solved approximately with guaranteed good quality. Our approaches achieve high performances with efficient costs. We present experimental results on various datasets.
Host: Prof. Evanthia Papadopoulou | |
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| | Dr. Danny Z. Chen received the B.S. degrees in Computer Science and in Mathematics from the University of San Francisco, California in 1985, and the M.S. and Ph.D. degrees in Computer Science from Purdue University, West Lafayette in 1988 and 1992, respectively. He is a Professor in the Department of Computer Science and Engineering at the University of Notre Dame. Dr. Chen’s main research interests include computational biomedicine, biomedical imaging, machine learning, data mining, computational geometry, algorithms, and VLSI. He has worked extensively with biomedical researchers and practitioners, published many papers in these areas, and holds 8 US patents for technology development in biomedical applications. He received the US NSF CAREER Award in 1996 and the 2017 PNAS Cozzarelli Prize of the US National Academy of Sciences. He is a Fellow of IEEE, a Fellow of AAAS, and is a Distinguished Scientist of ACM. 11:00 |
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