这篇论文提出BrReMark，通过先假设再验证的标注机制，大幅提升了脑MRI诊断的可信度和定位精度，值得关注。
BrReMark框架通过先假设异常并用边界框标注病灶区域，再重新检查验证，提升了空间可解释性。训练结合结构化推理轨迹的监督微调与强化学习，奖励定位准确性和诊断推理。采用域随机化病理合成增强策略，提升了对分布外数据的泛化性。内部基准上mAP50从0.74%提升至37.54%，临床F1达21.57%，诊断准确率45.26%。NOVA OOD基准上假阳性较当前最优方法减少45.7%，表明能有效降低对罕见病理的幻觉。
Enhancing Brain MRI Anomaly Detection and Reasoning with ROI Rethink and Synthetic Data
Medical vision-language models typically generate diagnoses through single-pass inference without indicating which image regions support their conclusions. This lack of spatial grounding limits clinical utility: outputs cannot be audited, and models may hallucinate findings on normal scans. We present BrReMark (Brain Rethink via ROI Marking), a framework that introduces explicit region marking into brain MRI diagnosis. The model first generates hypotheses about potential abnormalities and grounds them through explicit bounding box marking, then verifies conclusions by re-examining the marked evidence. Training combines supervised fine-tuning on structured reasoning trajectories with reinforcement learning using a composite reward over localization accuracy and diagnostic reasoning. Furthermore, we integrate a domain randomization-based pathology synthesis augmentation strategy to improve the model's generalizability to out-of-distribution (OOD) data. On internal benchmark, BrReMark improves mAP50 from 0.74% to 37.54% compared to the base model, while achieving 21.57% Clinical F1 and 45.26% diagnostic accuracy. On NOVA OOD benchmark, it also achieves competitive overall performance with a 45.7% reduction in false positives compared to the state-of-the-art, indicating reduced hallucination on rare pathologies. These findings suggest that explicit hypothesis-verification grounding is a practical path toward trustworthy open-ended brain MRI diagnosis across both in-distribution and OOD settings.