Anatomy-R1: 通过解剖相似性课程和群体多样性增强多模态大语言模型的解剖推理
Anatomy-R1: Enhancing Anatomy Reasoning in Multimodal Large Language Models via Anatomical Similarity Curriculum and Group Diversity Augmentation
AI总结：
Anatomy-R1通过解剖相似性课程和群体多样性增强方法提升多模态大语言模型在医学影像中的解剖推理能力。
摘要：
多模态大语言模型（MLLMs）在自然图像推理方面取得了显著进展，但其在医学影像中的潜力仍被忽视，尤其是在临床解剖手术图像中。解剖理解任务需要精确的理解和临床一致的答案，这由于医学数据的复杂性和高质量专家注释的稀缺而难以实现。这些挑战限制了传统监督微调（SFT）策略的有效性。尽管最近的工作表明组相对策略优化（GRPO）可以在不依赖大量数据的情况下增强MLLMs的推理能力，但我们发现两个弱点阻碍了GRPO在解剖识别中的推理性能：1）不同解剖结构之间的知识无法有效共享，导致信息获取不均，防止模型收敛；2）模型迅速收敛到单一推理路径，抑制了多样化策略的探索。为克服这些挑战，我们提出了两种新方法。首先，我们实现了一种渐进学习策略，称为解剖相似性课程学习，通过控制问题难度来控制答案选择的相似性，使模型能够逐步掌握复杂问题。其次，我们利用称为群体多样性问题增强的问题增强技术，扩展模型在困难查询中的搜索空间，减轻产生统一响应的倾向。在SGG-VQA和OmniMedVQA基准上的全面实验表明，我们的方法在两个基准上都取得了显著改进，证明了其在增强MLLMs医学推理能力方面的有效性。代码可在https://github.com/tomato996/Anatomy-R1找到。
英文摘要：
Multimodal Large Language Models (MLLMs) have achieved impressive progress in natural image reasoning, yet their potential in medical imaging remains underexplored, especially in clinical anatomical surgical images. Anatomy understanding tasks demand precise understanding and clinically coherent answers, which are difficult to achieve due to the complexity of medical data and the scarcity of high-quality expert annotations. These challenges limit the effectiveness of conventional Supervised Fine-Tuning (SFT) strategies. While recent work has demonstrated that Group Relative Policy Optimization (GRPO) can enhance reasoning in MLLMs without relying on large amounts of data, we find two weaknesses that hinder GRPO's reasoning performance in anatomy recognition: 1) knowledge cannot be effectively shared between different anatomical structures, resulting in uneven information gain and preventing the model from converging, and 2) the model quickly converges to a single reasoning path, suppressing the exploration of diverse strategies. To overcome these challenges, we propose two novel methods. First, we implement a progressive learning strategy called Anatomical Similarity Curriculum Learning by controlling question difficulty via the similarity of answer choices, enabling the model to master complex problems incrementally. Second, we utilize question augmentation referred to as Group Diversity Question Augmentation to expand the model's search space for difficult queries, mitigating the tendency to produce uniform responses. Comprehensive experiments on the SGG-VQA and OmniMedVQA benchmarks show our method achieves a significant improvement across the two benchmarks, demonstrating its effectiveness in enhancing the medical reasoning capabilities of MLLMs. The code can be found in https://github.com/tomato996/Anatomy-R1
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
