Diff-SiamNet: Breast Ultrasound Image Classification with Forward Diffusion and Discriminative Embedding

Abstract

Existing breast ultrasound image classification algorithms frequently perform poorly in real-world clinical settings due to image deterioration, semantic drift, and insufficient supervision, significantly limiting tiny lesion recognition and practical deployment. To address this critical issue, this paper proposes Diff-SiamNet, a lightweight discriminative diffusion network. We present a novel forward diffusion perturbation mechanism to emulate scattering noise and boundary blurring, enhancing the model’s feature robustness against low-quality images. Additionally, we develop a diffusion-aware attention gate (DAG) to dynamically integrate clear and degraded map features, thereby alleviating semantic discrepancies. The Triplet++ embedding strategy is designed to guide the model to form a discriminative space with inter-class separation and intra-class aggregation under weak annotation. On the BUSI dataset, Diff-SiamNet outperforms state-of-the-art ResNet, Swin Transformer and HGDF in five metrics, including accuracy (93.4%), AUC (94.2%), and F1-score (92.9%), which significantly improves the ability of recognizing fuzzy boundaries and tiny lesions. The method has good interpretability and deployment efficiency, and is expected to serve clinical intelligent screening in resource-constrained environments