CORE-Seg: Reasoning-Driven Segmentation for Complex Lesions via Reinforcement Learning

The paper introduces CORE‑Seg, an end‑to‑end framework that combines reasoning and segmentation for complex lesion detection in medical images. It leverages a Semantic‑Guided Prompt Adapter and a progressive training strategy from SFT to GRPO, using an adaptive dual‑granularity reward mechanism to address reward sparsity. CORE‑Seg achieves state‑of‑the‑art performance, with a mean Dice score of 37.06%—14.89% higher than the next best baseline—and reduces the failure rate to 18.42%.

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• llms
• computer-vision
• reinforcement-learning
• fine-tuning
• efficiency
• multimodal
• benchmarks
• safety