用放射科报告自动构建多粒度医学图像检索框架，提升精准度与灵活性。
RadIR: A Scalable Framework for Multi-Grained Medical Image Retrieval via Radiology Report Mining
- 通过密集报告挖掘实现图像相似性多粒度排序，全自动且可扩展。
- 构建了两个新数据集：MIMIC-IR（X光）和CTRATE-IR（CT），含结构化标注。
- 系统在78项指标中77项达顶尖水平，支持按解剖结构精准检索。
由于不同医疗场景下对‘相似图像’定义不一，构建先进的医学影像检索系统面临挑战。这一难题又因缺乏大规模、高质量的医学影像检索数据集与基准而加剧。本文提出一种新方法，利用密集放射科报告，在可扩展且完全自动化的条件下，定义多粒度的图像级相似性排序。基于此，我们构建了两个综合性医学影像检索数据集：针对胸部X光的MIMIC-IR和针对CT扫描的CTRATE-IR，提供基于多种解剖结构的详细图像-图像排序标注。此外，我们开发了两个检索系统：RadIR-CXR 和 model-ChestCT，其在传统的图像-图像与图像-报告检索任务中表现优异，并能灵活有效地根据文本描述的特定解剖结构进行图像检索，在78项指标中的77项达到当前最优水平。
原文摘要 · Abstract (English)
Developing advanced medical imaging retrieval systems is challenging due to the varying definitions of `similar images' across different medical contexts. This challenge is compounded by the lack of large-scale, high-quality medical imaging retrieval datasets and benchmarks. In this paper, we propose a novel methodology that leverages dense radiology reports to define image-wise similarity ordering at multiple granularities in a scalable and fully automatic manner. Using this approach, we construct two comprehensive medical imaging retrieval datasets: MIMIC-IR for Chest X-rays and CTRATE-IR for CT scans, providing detailed image-image ranking annotations conditioned on diverse anatomical structures. Furthermore, we develop two retrieval systems, RadIR-CXR and model-ChestCT, which demonstrate superior performance in traditional image-image and image-report retrieval tasks. These systems also enable flexible, effective image retrieval conditioned on specific anatomical structures described in text, achieving state-of-the-art results on 77 out of 78 metrics.
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