Early and precise classification of breast cancer using ultrasound (US) imaging plays a vital role in reducing diagnostic delays and improving patient outcomes. However, the inherently low contrast, speckle noise, and operator variability associated with US images pose significant challenges to conventional computer-aided diagnostic systems. To address these limitations, we propose a selective, uncertainty-aware Bayesian deep learning framework for automated classification of breast lesions using the Breast Ultrasound Images (BUSI) dataset. Our approach comprises three key components. First, we use a stratified Train/Validation/Test split before augmentation and apply class-aware augmentation only to the training set to support balanced learning and generalization. Second, the classification core is a hybrid ensemble combining ConvNeXt-Tiny and EfficientFormer-L1, trained with Focal Loss, mixup augmentation, and Monte Carlo Dropout to model epistemic uncertainty. Third, we apply a selective Bayesian inference strategy employing Monte Carlo Dropout, temperature scaling, and uncertainty-based filtering, to retain high-certainty predictions. Experimental results show 93.16% accuracy and 0.928 macro-F1 under standard unfiltered evaluation (bootstrap 95% CI: 0.880, 0.974). Across five seeds, the final reported test results correspond to the aggregated multi-seed ensemble, and uncertainty is summarized using bootstrap confidence intervals. Using validation-derived confidence/entropy thresholds, selective inference achieves 97.26% accuracy at 62.4% coverage (73/117 retained) while maintaining performance across all three classes, including Normal cases. We further evaluate the framework on two external breast ultrasound datasets (BUS-BRA and BUS-UCLM) to assess robustness under domain shift. Overall, these results suggest that combining Bayesian uncertainty estimation with selective prediction improves the reliability and clinical practicality of breast ultrasound decision support.
Ali et al. (Sat,) studied this question.