标题： MedVQA-TREE：用于肌少症预测的多模态推理和检索框架 显示英文标题

标题： MedVQA-TREE: A Multimodal Reasoning and Retrieval Framework for Sarcopenia Prediction

摘要： 通过超声进行准确的肌少症诊断仍然具有挑战性，这是由于影像学线索细微、标记数据有限，以及大多数模型中缺乏临床背景。 我们提出了MedVQA-TREE，这是一种多模态框架，结合了分层图像解释模块、门控特征级融合机制和一种新颖的多跳、多查询检索策略。 视觉模块包括解剖分类、区域分割和基于图的空间推理，以捕捉粗粒度、中层和细粒度结构。 门控融合机制选择性地将视觉特征与文本查询集成，而临床知识则通过UMLS引导的管道访问PubMed和一个特定于肌少症的外部知识库。 MedVQA-TREE在两个公开的MedVQA数据集（VQA-RAD和PathVQA）和一个自定义的肌少症超声数据集上进行了训练和评估。 该模型实现了高达99%的诊断准确率，并且比之前最先进的方法提高了超过10%。 这些结果强调了将结构化的视觉理解与引导的知识检索相结合在肌少症有效人工智能辅助诊断中的优势。 显示英文摘要

摘要： Accurate sarcopenia diagnosis via ultrasound remains challenging due to subtle imaging cues, limited labeled data, and the absence of clinical context in most models. We propose MedVQA-TREE, a multimodal framework that integrates a hierarchical image interpretation module, a gated feature-level fusion mechanism, and a novel multi-hop, multi-query retrieval strategy. The vision module includes anatomical classification, region segmentation, and graph-based spatial reasoning to capture coarse, mid-level, and fine-grained structures. A gated fusion mechanism selectively integrates visual features with textual queries, while clinical knowledge is retrieved through a UMLS-guided pipeline accessing PubMed and a sarcopenia-specific external knowledge base. MedVQA-TREE was trained and evaluated on two public MedVQA datasets (VQA-RAD and PathVQA) and a custom sarcopenia ultrasound dataset. The model achieved up to 99% diagnostic accuracy and outperformed previous state-of-the-art methods by over 10%. These results underscore the benefit of combining structured visual understanding with guided knowledge retrieval for effective AI-assisted diagnosis in sarcopenia.