Boosting Ultrasound Image Classification via Attribute-Guided Dual-Branch Framework
Quick Answer
The proposed attribute-guided dual-branch framework enhances ultrasound image classification by integrating domain-agnostic medical attributes, improving both accuracy and interpretability.
Quick Take
The proposed attribute-guided dual-branch framework enhances ultrasound image classification by integrating domain-agnostic medical attributes, improving both accuracy and interpretability. This method can be seamlessly integrated into existing architectures, demonstrating consistent performance improvements across various classification tasks with minimal overhead.
Key Points
- Introduces a medical-prior module for enhanced diagnostic performance.
- Baseline branch uses conventional architectures for image category prediction.
- Attribute-guided branch provides human-interpretable decision cues.
- Adaptive decision module fuses outputs from both branches for final predictions.
- Code available at GitHub for implementation and testing.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2607. 01648v1 Announce Type: new 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.
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