用医学属性引导双分支框架提升超声图像分类准确率与可解释性
Boosting Ultrasound Image Classification via Attribute-Guided Dual-Branch Framework
- 引入无领域限制的医学属性先验，增强模型泛化能力
- 双分支融合策略使分类准确率显著提升且决策过程可解释
- 适用于多种超声任务，适配现有模型无需复杂改造
超声图像分类对辅助诊断至关重要，但现有方法常忽略临床先验，在复杂场景下泛化能力差，且缺乏可解释性，限制了临床应用。为此，本文提出一种属性引导的双分支框架，通过引入无领域限制的医学属性先验，提升分类性能并提供可解释的判别依据。基础分支沿用传统架构，通过全连接层预测类别；属性引导分支注入医学属性先验，生成人类可理解的决策线索。最后，自适应决策模块以数据相关方式融合两分支输出，实现最终预测。在多个超声分类任务上的实验表明，该方法可无缝集成至多种骨干网络和先进模型，开销低，持续提升准确率与可解释性。代码已公开：https://github.com/zhaobo253-crypto/AttrGuide。
原文摘要 · Abstract (English)
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.
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