WristMIR: 儿童手腕X光片的粗到细区域感知检索框架，基于放射科报告驱动学习
WristMIR: Coarse-to-Fine Region-Aware Retrieval of Pediatric Wrist Radiographs with Radiology Report-Driven Learning
AI总结：
WristMIR通过结合放射科报告和骨特定定位，实现儿童手腕X光片的粗到细区域感知检索，提升骨折诊断的准确性和临床相关性。
摘要：
在临床中，检索具有类似骨折模式的手腕X光片具有挑战性，因为重要的临床线索是微妙的、高度局部化的，常常被重叠的解剖结构或变化的成像视角所遮挡。进展进一步受到缺乏大量、标注良好的用于基于病例的医学图像检索的数据集的限制。我们介绍了WristMIR，一个区域感知的儿童手腕X光片检索框架，该框架利用密集的放射科报告和骨特定的定位来学习细粒度、具有临床意义的图像表示，而无需任何手动图像级标注。使用基于MedGemma的结构报告挖掘技术生成全局和区域级的描述，结合预处理的手腕图像和针对桡骨、尺骨和尺骨茎突的骨特定裁剪，WristMIR联合训练全局和局部对比编码器，并执行两阶段检索过程：（1）粗略的全局匹配以识别候选检查，随后是（2）区域条件的重新排序，与预定义的解剖骨区域对齐。WristMIR在强大的视觉-语言基线之上提升了检索性能，将图像到文本的Recall@5从0.82%提升到9.35%。其嵌入也产生了更强的骨折分类（AUROC 0.949，AUPRC 0.953）。在区域感知评估中，两阶段设计显著提高了基于检索的骨折诊断，将平均$F_1$从0.568提升到0.753，并且放射科医生认为其检索的病例更加具有临床相关性，平均评分从3.36提升到4.35。这些发现突显了解剖引导检索在增强诊断推理和支持儿科骨科影像学临床决策中的潜力。源代码在https://github.com/quin-med-harvard-edu/WristMIR上公开。
英文摘要：
Retrieving wrist radiographs with analogous fracture patterns is challenging because clinically important cues are subtle, highly localized and often obscured by overlapping anatomy or variable imaging views. Progress is further limited by the scarcity of large, well-annotated datasets for case-based medical image retrieval. We introduce WristMIR, a region-aware pediatric wrist radiograph retrieval framework that leverages dense radiology reports and bone-specific localization to learn fine-grained, clinically meaningful image representations without any manual image-level annotations. Using MedGemma-based structured report mining to generate both global and region-level captions, together with pre-processed wrist images and bone-specific crops of the distal radius, distal ulna, and ulnar styloid, WristMIR jointly trains global and local contrastive encoders and performs a two-stage retrieval process: (1) coarse global matching to identify candidate exams, followed by (2) region-conditioned reranking aligned to a predefined anatomical bone region. WristMIR improves retrieval performance over strong vision-language baselines, raising image-to-text Recall@5 from 0.82% to 9.35%. Its embeddings also yield stronger fracture classification (AUROC 0.949, AUPRC 0.953). In region-aware evaluation, the two-stage design markedly improves retrieval-based fracture diagnosis, increasing mean $F_1$ from 0.568 to 0.753, and radiologists rate its retrieved cases as more clinically relevant, with mean scores rising from 3.36 to 4.35. These findings highlight the potential of anatomically guided retrieval to enhance diagnostic reasoning and support clinical decision-making in pediatric musculoskeletal imaging. The source code is publicly available at https://github.com/quin-med-harvard-edu/WristMIR.
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。
