Radir: A Scalable Framework For Multi-grained Medical Image Retrieval Via Radiology Report Mining
2025 · Tengfei Zhang, Ziheng Zhao, Chaoyi Wu, et al.
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 describ
Authors
(none)
Tags
Stats
Related papers
- Radiomicsretrieval: A Customizable Framework For Medical Image Retrieval Using Radiomics Features (2025)2.29
- 3D-MIR: A Benchmark And Empirical Study On 3D Medical Image Retrieval In Radiology (2023)0.00
- Content-based 3D Image Retrieval And A Colbert-inspired Re-ranking For Tumor Flagging And Staging (2025)0.00
- BIMCV-R: A Landmark Dataset For 3D CT Text-image Retrieval (2024)8.09
- Grounded Multimodal Retrieval-augmented Drafting Of Radiology Impressions Using Case-based Similarity Search (2026)0.00
- Ontology-based Concept Distillation For Radiology Report Retrieval And Labeling (2025)1.20
- X-TRA: Improving Chest X-ray Tasks With Cross-modal Retrieval Augmentation (2023)8.09
- Wristmir: Coarse-to-fine Region-aware Retrieval Of Pediatric Wrist Radiographs With Radiology Report-driven Learning (2026)0.00