CORE-Seg: Reasoning-Driven Segmentation for Complex Lesions via Reinforcement Learning
Medical image segmentation is undergoing a paradigm shift from conventional visual pattern matching to cognitive reasoning analysis. Although Multimodal Large Language Models (MLLMs) have shown promise in integrating linguistic and visual knowledge, significant gaps remain: existing general MLLMs possess broad common sense but lack the specialized visual reasoning required for complex lesions, whereas traditional segmentation models excel at pixel-level segmentation but lack logical interpretability. In this paper, we introduce ComLesion-14K, the first diverse Chain-of-Thought (CoT) benchmark for reasoning-driven complex lesion segmentation. To accomplish this task, we propose CORE-Seg, a...
| Authors | Yuxin Xie, Yuming Chen, Yishan Yang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 6 March 2026 |
| Last updated at source | 9 October 2026 |
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