Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in the deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. In this paper, we provide an extensive survey of deep learning-based breast cancer imaging research, covering studies on mammogram, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods, publicly available datasets, and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are described in detail. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging.

翻译：自2020年以来，乳腺癌在全球恶性肿瘤中的发病率已跃居首位。乳腺癌影像学检查在早期诊断和干预中发挥着关键作用，能够有效改善患者预后。过去十年间，深度学习在乳腺癌影像分析领域取得了显著进展，在阐释影像模态的丰富信息与复杂语境方面展现出巨大潜力。鉴于深度学习技术的快速演进与乳腺癌日益严峻的发病形势，总结过往成果并识别未来亟待解决的挑战至关重要。本文对近十年来基于深度学习的乳腺癌影像研究进行了系统综述，涵盖乳腺X线摄影、超声、磁共振成像及数字病理图像等相关研究。我们详细阐述了主要的深度学习方法、公开数据集以及基于影像的筛查、诊断、治疗反应预测与预后评估等应用。基于本综述的发现，我们深入讨论了深度学习在乳腺癌影像中面临的挑战及未来潜在研究方向。