Application of deep learning technology in breast cancer: a systematic review of segmentation, detection, and classification approaches

Abstract

Deep learning offers considerable promise to support early detection, risk stratification and workflow efficiency across breast imaging modalities, with CNNs and Transformers providing complementary strengths for local fine-detail versus global contextual modelling. Nevertheless, the current evidence base is constrained by heterogeneous designs, limited reporting of study quality and biased datasets, so reported performance should not be interpreted as definitive proof of clinical readiness. Future research should prioritise multi-centre, demographically diverse cohorts, transparent quality assessment, external and prospective validation, and evaluation of reader and workflow impact. Developing explainable, fairness-aware and privacy-preserving systems-such as those enabled by interpretable architectures and federated learning-will be essential for safe and equitable translation of deep learning tools into routine breast cancer care.

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