Externally Validated Breast Ultrasound Segmentation via Multi-task Learning with BI-RADS-Consistent Morphological Priors

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arXiv:2511.15968v2 Announce Type: replace-cross Abstract: External validation of breast ultrasound segmentation models remains limited because internal train--test splits do not capture domain shifts across imaging systems, acquisition protocols, and patient populations. We introduce a novel multi-task framework for breast ultrasound segmentation and malignancy classification. Its central methodological advance is a differentiable morphology-to-malignancy bridge: lesion area, boundary roughness, compactness, and texture are computed from the predicted soft segmentation mask, aggregated with learned weights into a morphology-based malignancy score, and constrained to agree with the image-level classifier. To our knowledge, this is the first breast ultrasound framework to use BI-RADS-inspired morphology derived from its own soft segmentation output as an end-to-end consistency target for malignancy classification. It is also the first 2D B-mode multi-task study to report every directed external transfer among four independent datasets: training on each dataset and testing on the other three yields 12 source--target pairs, assessed with single models and five-fold ensembles. In matched comparisons across all pairs, the proposed single-model configuration outperforms dedicated single-task baselines in segmentation (DC: 0.764 vs. 0.740) and malignancy classification (AUC: 0.818 vs. 0.791). The ensemble achieves a mean external DC of 0.786 and is competitive with SAM-based segmentation methods using an EfficientNet-B7 encoder while also predicting malignancy. The learned …

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