CS Events

Qualifying Exam Toward Universal Medical Image Segmentation
Export Event Save iCal Send to Google Calendar Send to Yahoo Calendar Send to Outlook Live Send to Microsoft Outlook Save iCal Preserve formatting in description (only supported in some calendar applications) Close Thursday, December 07, 2023, 03:00pm - 05:00pm

Speaker: Yunhe Gao

Location : CoRE 305

Committee: 

Professor Dimitris Metaxas (Chair)

Assistant Professor Hao Wang

Assistant Professor Yongfeng Zhang

Assistant Professor Karthik Srikanta

Event Type: Qualifying Exam

Abstract:  A major enduring focus of clinical workflows is disease analytics and diagnosis, leading to medical imaging datasets where the modalities and annotations are strongly tied to specific clinical objectives. To date, the prevailing training paradigm for medical image segmentation revolves around developing separate models for specific medical objects (e.g., organs or tumors) and image modalities (e.g., CT or MR). This traditional paradigm can hinder the robustness and generalizability of these AI models, inflate costs when further scaling data volumes, and fail to exploit potential synergies among various medical imaging tasks. By observing the training program of radiology residency, we recognize that radiologists’ expertise arises from routine exposure to a diverse range of medical images across body regions, diseases, and imaging modalities. This observation motivates us to explore a new training paradigm, “universal medical image segmentation”, whose key goal is to learn from diverse medical imaging sources. In the qualification exam, I’ll delve into challenges in the new paradigm including issues with partial labeling, conflicting class definitions, and significant data heterogeneity. I’ll also present our work, aimed at tackling these challenges. We demonstrate that our proposed universal paradigm not only offers enhanced performance and scalability, but also excels in transfer learning, incremental learning and generalization. This innovative approach opens up new perspectives for the construction of foundational models in a broad range of medical image analysis.

: 

Contact  Professor Dimitris Metaxas (Chair)