Application of deep learning technology in breast cancer: a systematic review of segmentation, detection, and classification approaches
Abstract
OBJECTIVE: To provide a critical and clinically oriented synthesis of recent deep learning developments for breast cancer imaging across major modalities, with emphasis on model architectures, dataset characteristics, methodological quality, and implications for clinical translation.
METHODS: Following PRISMA guidelines, we systematically searched PubMed, Scopus, Web of Science, ScienceDirect, and Google Scholar for studies published from 2020 to 2024 on deep learning applied to breast imaging. Sixty-five studies using convolutional neural networks (CNNs), Transformers, or hybrid architectures were included. Datasets were comparatively profiled, and study quality and risk of bias were appraised using QUADAS-2.
RESULTS: CNN-based classifiers, particularly on mammography and pathology, commonly achieved median accuracies above 90% and AUCs around or above 0.95, while CNN detectors reported high sensitivities and mid-90% accuracies, supporting their potential role as second readers. CNN-derived U-Net variants dominated segmentation tasks, yielding high Dice and IoU values for tumour and fibroglandular-tissue delineation. Transformer and hybrid models showed advantages when global context, multi-view inputs or volumetric data were critical (e.g. dense breasts, DBT, DCE-MRI), where they improved lesion localisation and patient-level risk stratification. However, QUADAS-2 and dataset profiling revealed substantial limitations: most studies were retrospective, single-centre and…
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