Qiaochu Zhao

To evaluate and optimize segmentation pipeline configurations for head-and-neck organ segmentation to improve accuracy, robustness, and clinical applicability.

To generalize decentralized mutual learning to multi-class 3D organ-at-risk (OAR) segmentation and evaluate whether gossip-based peer-to-peer training improves federated radiotherapy auto-segmentation across heterogeneous clinical sites.

To evaluate the robustness and generalizability of Det2Prompt, a foundation-model driven framework for partially-label medical image segmentation, under various annotation conditions.