Computer Science > Computer Vision and Pattern Recognition
arXiv:2603.09108 (cs)
[Submitted on 10 Mar 2026]
Title:Composed Vision-Language Retrieval for Skin Cancer Case Search via Joint Alignment of Global and Local Representations
Authors:Yuheng Wang, Yuji Lin, Dongrun Zhu, Jiayue Cai, Sunil Kalia, Harvey Lui, Chunqi Chang, Z. Jane Wang, Tim K. Lee
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Abstract:Medical image retrieval aims to identify clinically relevant lesion cases to support diagnostic decision making, education, and quality control. In practice, retrieval queries often combine a reference lesion image with textual descriptors such as dermoscopic features. We study composed vision-language retrieval for skin cancer, where each query consists of an image to text pair and the database contains biopsy-confirmed, multi-class disease cases. We propose a transformer based framework that learns hierarchical composed query representations and performs joint global-local alignment between queries and candidate images. Local alignment aggregates discriminative regions via multiple spatial attention masks, while global alignment provides holistic semantic supervision. The final similarity is computed through a convex, domain-informed weighting that emphasizes clinically salient local evidence while preserving global consistency. Experiments on the public Derm7pt dataset demonstrate consistent improvements over state-of-the-art methods. The proposed framework enables efficient access to relevant medical records and supports practical clinical deployment.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2603.09108 [cs.CV] |
| (or arXiv:2603.09108v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09108
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View a PDF of the paper titled Composed Vision-Language Retrieval for Skin Cancer Case Search via Joint Alignment of Global and Local Representations, by Yuheng Wang and 8 other authors
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