Abstract: |
With advancements in deep learning and AI techniques, medical image segmentation has experienced rapid development over the past decade. Modern DL-based models, utilizing large labeled datasets, often produce impressive benchmark results. However, practical issues, such as reliability and trustworthiness, persist when these models are implemented in hospitals and medical facilities. This talk addresses two related aspects of medical image segmentation for improving model deployment: model evaluation and test-time methods. First, we will discuss our recent work on deployment-centric model evaluation, evaluation of foundation models and related techniques. Next, we will cover a series of test-time methods that we have developed to improve video segmentation consistency, enhance the quality of medical image segmentation, and more recently, advance segmenting anything in medical images. Finally, we will briefly highlight several other projects from my group and discuss directions in medical image segmentation research that we find promising and important. |
Biography: |
| Yizhe Zhang, Ph.D., is an associate professor at Nanjing University of Science and Technology. He received his Ph.D. from the University of Notre Dame in the United States. Before returning to Nanjing, he was a senior research engineer at Qualcomm AI Research, San Diego, where he worked on efficient video segmentation and the spatiotemporal consistency of segmentation. He has conducted research on topics such as active learning, semi-supervised learning, model design, training and evaluation in medical image segmentation. As the first author, he has published papers in conferences and journals including MICCAI, Medical Image Analysis, IEEE TMI, BIBM, ICCV, AAAI, and WACV. As a key contributor, he was involved in biomedical image modeling and analysis work that won the 2017 Cozzarelli Prize awarded by the National Academy of Sciences. |