用强化学习让模型像医生一样逐步聚焦病灶，提升CT诊断准确率
MedReason-R1: Learning to Reason for CT Diagnosis with Reinforcement Learning and Local Zoom
- 通过局部放大病灶区域并结合强化学习推理，模拟医生由粗到细的诊断过程
- 在8.4万条CT问答数据上达到当前最佳诊断性能，显著优于通用与医疗模型
- 适合医学影像分析、辅助诊断系统研发人员参考，尤其关注可解释性与泛化能力
通用大视觉语言模型（VLM）在自然图像描述上表现优异，但在医学领域仍不理想，尤其面对简单任务时，主要因缺乏大规模高质量医学影像数据集，且忽视从全局到局部的渐进式诊断流程。为此，我们构建了包含8.4万条问答对的CT-RATE-VQA数据集。针对诊断过程问题，提出MedReason-R1，一种具备显式推理链的医疗VLM。该模型创新性地将病灶感兴趣区域局部放大嵌入图像，强调全局定位与疾病特异性细节的协同作用。同时引入GRPO强化学习框架，实现无需人工标注的高效推理。相比近期通用及医疗VLM，MedReason-R1在CT疾病诊断上达到最先进水平，且保持良好泛化能力。代码、模型权重与数据集已开源。
原文摘要 · Abstract (English)
General-purpose large Vision-Language Models (VLMs) demonstrate strong capabilities in generating detailed descriptions for natural images. However, their performance in the medical domain remains suboptimal, even for relatively straightforward tasks, primarily due to the lack of large-scale, high-quality, specialized medical imaging datasets and the neglect of the diagnostic process that progresses from coarse to fine-grained. To address the first issue, we construct the CT-RATE-VQA dataset, which has 84K QA pairs. For the second issue, we propose MedReason-R1, a medical VLM with explicit reasoning process for disease diagnosis. MedReason-R1 incorporates a novel strategy that embeds zoom-in disease region-of-interest areas into the image, highlighting the crucial role of both global localization and disease-specific details in enhancing the model's diagnostic performance. Furthermore, we introduce the GRPO reinforcement learning framework to MedReason-R1, which enables effective reasoning without relying on costly manual annotations. Compared to recent general-purpose and medical VLMs, MedReason-R1 achieves state-of-the-art performance in CT disease diagnosis while retaining generalization. The code, checkpoints, and dataset are available at: https://github.com/Leevan001/MedReason-R1
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