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，乳腺癌在全球所有恶性肿瘤中发病率已升至最高。乳腺影像学在早期诊断与干预中发挥关键作用，有助于改善乳腺癌患者的预后。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，在解读乳腺影像模式的丰富信息与复杂背景方面展现出巨大潜力。鉴于深度学习技术的快速进步以及乳腺癌日益严峻的形势，总结既往进展并识别未来亟需应对的挑战至关重要。本文对基于深度学习的乳腺癌影像学研究进行了广泛综述，涵盖过去十年间在乳腺X线摄影、超声、磁共振成像及数字病理图像方面的研究。详细阐述了主要深度学习方法、公开可用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，我们就深度学习在乳腺癌影像学中面临的研究挑战及潜在未来方向进行了全面讨论。