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年起，乳腺癌已成为全球所有恶性肿瘤中发病率最高的癌症。乳腺影像学在早期诊断与干预中发挥着重要作用，可有效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，在解读乳腺影像模态的丰富信息与复杂情境方面展现出巨大潜力。鉴于深度学习技术的飞速发展以及乳腺癌日益严峻的现状，总结过往成果并明确亟待解决的未来挑战至关重要。本文对基于深度学习的乳腺癌影像研究进行了广泛综述，涵盖了过去十年中针对乳腺X线摄影、超声、磁共振成像及数字病理图像的研究。我们详细阐述了主要的深度学习方法、公开可用的数据集，以及其在影像筛查、诊断、治疗反应预测和预后评估中的应用。基于本综述的研究发现，我们全面讨论了基于深度学习的乳腺癌影像研究中面临的挑战与未来可能的探索方向。