# Boosting Ultrasound Image Classification via Attribute-Guided Dual-Branch Framework

arXiv: https://arxiv.org/abs/2607.01648 · HTML: https://mlsanity.com/paper/2607.01648/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 2 Jul 2026.

## Builds on (most cited first, 12 of 12)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Concept Bottleneck Models](https://mlsanity.com/paper/2007.04612/) (2020)
- [Post-hoc Concept Bottleneck Models](https://mlsanity.com/paper/2205.15480/) (2022)
- [Label-Free Concept Bottleneck Models](https://mlsanity.com/paper/2304.06129/) (2023)
- [TransZero: Attribute-guided Transformer for Zero-Shot Learning](https://mlsanity.com/paper/2112.01683/) (2021)
- [Mammo-CLIP: A Vision Language Foundation Model to Enhance Data Efficiency and Robustness in Mammography](https://mlsanity.com/paper/2405.12255/) (2024)
- [Aligning Human Knowledge with Visual Concepts Towards Explainable Medical Image Classification](https://mlsanity.com/paper/2406.05596/) (2024)
- [Aligning Medical Images with General Knowledge from Large Language Models](https://mlsanity.com/paper/2409.00341/) (2024)
- [Vision Mamba for Classification of Breast Ultrasound Images](https://mlsanity.com/paper/2407.03552/) (2024)
- [XFMamba: Cross-Fusion Mamba for Multi-View Medical Image Classification](https://mlsanity.com/paper/2503.02619/) (2025)
- [UniUSNet: A Promptable Framework for Universal Ultrasound Disease Prediction and Tissue Segmentation](https://mlsanity.com/paper/2406.01154/) (2024)
- [Hybrid-View Attention Network for Clinically Significant Prostate Cancer Classification in Transrectal Ultrasound](https://mlsanity.com/paper/2507.03421/) (2025)

## Abstract (arXiv)

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.