AnatomiX：一种解剖学感知的胸部X光解读多模态大语言模型
AnatomiX, an Anatomy-Aware Grounded Multimodal Large Language Model for Chest X-Ray Interpretation
AI总结：
提出AnatomiX，一种两阶段解剖学感知多模态大语言模型，通过先识别解剖结构再执行下游任务，在解剖定位、短语定位、定位诊断和定位描述任务上相比现有方法提升超过25%。
摘要：
多模态医学大语言模型在胸部X光解读方面取得了显著进展，但在空间推理和解剖学理解方面仍面临挑战。尽管现有的定位技术提高了整体性能，但它们往往未能建立真正的解剖对应关系，导致医学领域中的解剖理解错误。为弥补这一差距，我们引入了AnatomiX，一种用于解剖学定位的胸部X光解读的多任务多模态大语言模型。受放射学工作流程启发，AnatomiX采用两阶段方法：首先识别解剖结构并提取其特征，然后利用大语言模型执行多种下游任务，如短语定位、报告生成、视觉问答和图像理解。在多个基准上的大量实验表明，与现有方法相比，AnatomiX实现了卓越的解剖推理，并在解剖定位、短语定位、定位诊断和定位描述任务上性能提升超过25%。代码和预训练模型可在 https://aneesurhashmi.github.io/anatomix 获取。
英文摘要：
Multimodal medical large language models have shown substantial progress in chest X-ray interpretation but continue to face challenges in spatial reasoning and anatomical understanding. Although existing grounding techniques improve overall performance, they often fail to establish a true anatomical correspondence, resulting in incorrect anatomical understanding in the medical domain. To address this gap, we introduce AnatomiX, a multitask multimodal large language model for anatomically grounded chest X-ray interpretation. Inspired by the radiological workflow, AnatomiX adopts a two stage approach: first, it identifies anatomical structures and extracts their features, and then leverages a large language model to perform diverse downstream tasks such as phrase grounding, report generation, visual question answering, and image understanding. Extensive experiments across multiple benchmarks demonstrate that AnatomiX achieves superior anatomical reasoning and delivers over 25% improvement in performance on anatomy grounding, phrase grounding, grounded diagnosis and grounded captioning tasks compared to existing approaches. Code and pretrained model are available at https://aneesurhashmi.github.io/anatomix
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。
