{"ID":5675099,"CreatedAt":"2026-07-03T01:40:09.565152011Z","UpdatedAt":"2026-07-05T03:20:12.707043107Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.01648","arxiv_id":"2607.01648","title":"Boosting Ultrasound Image Classification via Attribute-Guided Dual-Branch Framework","abstract":"Ultrasound image classification is essential for computer-aided diagnosis. However, current methods often neglect clinical priors, leading to poor generalization in challenging scenarios and a lack of interpretability that limits clinical adoption. To address these issues, we aim to develop a medical-prior module that can be seamlessly integrated into existing pipelines to enhance both diagnostic performance and interpretability. In this paper, we propose an attribute-guided dual-branch framework for ultrasound classification that introduces domain-agnostic medical attribute priors, improving generalization while offering interpretable evidence. Specifically, a baseline branch follows conventional architectures and predicts image categories via a fully connected classifier. An attribute-guided branch injects domain-agnostic attributes as priors and produces human-interpretable decision cues. Finally, an adaptive decision module fuses the two branches in a data-dependent manner to yield the final prediction. Experiments across diverse ultrasound classification tasks demonstrate that our approach can be integrated into multiple backbones and state-of-the-art methods with low overhead, consistently improving accuracy and interpretability. Code is available at: https://github.com/zhaobo253-crypto/AttrGuide.","short_abstract":"Ultrasound image classification is essential for computer-aided diagnosis. However, current methods often neglect clinical priors, leading to poor generalization in challenging scenarios and a lack of interpretability that limits clinical adoption. To address these issues, we aim to develop a medical-prior module that...","url_abs":"https://arxiv.org/abs/2607.01648","url_pdf":"https://arxiv.org/pdf/2607.01648v1","authors":"[\"Bo Zhao\",\"Yapeng Li\",\"Juhua Liu\",\"Bo Du\"]","published":"2026-07-02T03:20:12Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false,"code_links":[{"ID":613873,"CreatedAt":"2026-07-03T01:40:09.565152011Z","UpdatedAt":"2026-07-03T01:40:09.565152011Z","DeletedAt":null,"paper_id":5675099,"paper_url":"https://arxiv.org/abs/2607.01648","paper_title":"Boosting Ultrasound Image Classification via Attribute-Guided Dual-Branch Framework","repo_url":"https://github.com/zhaobo253-crypto/AttrGuide","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}