Subtyping Breast Lesions via Generative Augmentation based Long-tailed Recognition in Ultrasound

• URL: http://arxiv.org/abs/2507.22568v1
• Date: Wed, 30 Jul 2025 10:50:41 GMT
• Title: Subtyping Breast Lesions via Generative Augmentation based Long-tailed Recognition in Ultrasound
• Authors: Shijing Chen, Xinrui Zhou, Yuhao Wang, Yuhao Huang, Ao Chang, Dong Ni, Ruobing Huang,
• Abstract summary: We propose a framework for long-tailed classification that mitigates distributional bias through high-fidelity data synthesis.<n>Our method achieves promising performance compared to state-of-the-art approaches.
• Score: 8.410718166932798
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate identification of breast lesion subtypes can facilitate personalized treatment and interventions. Ultrasound (US), as a safe and accessible imaging modality, is extensively employed in breast abnormality screening and diagnosis. However, the incidence of different subtypes exhibits a skewed long-tailed distribution, posing significant challenges for automated recognition. Generative augmentation provides a promising solution to rectify data distribution. Inspired by this, we propose a dual-phase framework for long-tailed classification that mitigates distributional bias through high-fidelity data synthesis while avoiding overuse that corrupts holistic performance. The framework incorporates a reinforcement learning-driven adaptive sampler, dynamically calibrating synthetic-real data ratios by training a strategic multi-agent to compensate for scarcities of real data while ensuring stable discriminative capability. Furthermore, our class-controllable synthetic network integrates a sketch-grounded perception branch that harnesses anatomical priors to maintain distinctive class features while enabling annotation-free inference. Extensive experiments on an in-house long-tailed and a public imbalanced breast US datasets demonstrate that our method achieves promising performance compared to state-of-the-art approaches. More synthetic images can be found at https://github.com/Stinalalala/Breast-LT-GenAug.

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