AI RESEARCH
TP-Seg: Task-Prototype Framework for Unified Medical Lesion Segmentation
arXiv CS.CV
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ArXi:2604.00684v1 Announce Type: new Building a unified model with a single set of parameters to efficiently handle diverse types of medical lesion segmentation has become a crucial objective for AI-assisted diagnosis. Existing unified segmentation approaches typically rely on shared encoders across heterogeneous tasks and modalities, which often leads to feature entanglement, gradient interference, and suboptimal lesion discrimination. In this work, we propose TP-Seg, a task-prototype framework for unified medical lesion segmentation.