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WristMIR: Coarse-to-Fine Region-Aware Retrieval of Pediatric Wrist Radiographs with Radiology Report-Driven Learning
Date
2026-01-01
Author
Sonmezer, Mert
Vasylechko, Serge
Atasoy, Duygu
Ertekin Bolelli, Şeyda
Kurugol, Sila
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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, Wrist-MIR 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 F1 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.†
Subject Keywords
Contrastive pretraining
,
Pediatric Wrist radiography
,
Region-aware retrieval
URI
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=105040138743&origin=inward
https://hdl.handle.net/11511/119517
Conference Name
9th International Conference on Medical Imaging with Deep Learning, MIDL 2026
Collections
Department of Computer Engineering, Conference / Seminar
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BibTeX
M. Sonmezer, S. Vasylechko, D. Atasoy, Ş. Ertekin Bolelli, and S. Kurugol, “WristMIR: Coarse-to-Fine Region-Aware Retrieval of Pediatric Wrist Radiographs with Radiology Report-Driven Learning,” Chientan, Tayvan, 2026, vol. 315, Accessed: 00, 2026. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=105040138743&origin=inward.