Subtyping Breast Lesions via Generative Augmentation based Long-tailed Recognition in Ultrasound
This work addresses the challenge of automated breast lesion subtype recognition in ultrasound for medical diagnosis, which is incremental as it builds on existing generative augmentation techniques.
The paper tackles the problem of classifying breast lesion subtypes in ultrasound images with a long-tailed data distribution by proposing a dual-phase framework that uses generative augmentation and reinforcement learning to balance data. The method achieves promising performance on both in-house and public datasets compared to state-of-the-art approaches.
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.