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arXiv · 2503.04653

RadIR: A Scalable Framework for Multi-Grained Medical Image Retrieval via Radiology Report Mining

Tengfei Zhang·Ziheng Zhao·Chaoyi Wu·Xiao Zhou·Ya Zhang·Yanfeng Wang·Weidi Xie
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Abstract

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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Tengfei Zhang, Ziheng Zhao, Chaoyi Wu, Xiao Zhou, Ya Zhang, Yanfeng Wang, Weidi Xie. 2025-07-12. RadIR: A Scalable Framework for Multi-Grained Medical Image Retrieval via Radiology Report Mining. https://arxiv.org/abs/2503.04653

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