Abstract: Breast cancer is the most common malignant tumor in women and its early detection is decisive. Breast imaging plays an important role in early detection of breast cancer as well as monitoring and evaluation during treatment, but manual detection of medical images is usually time-consuming and labor-intensive. Recently, deep learning algorithms have made significant progress in early breast cancer diagnosis. By combing the relevant literature in recent years, a systematic review of the application of deep learning techniques in breast cancer diagnosis with different imaging modalities is conducted, aiming to provide a reference for in-depth research on deep learning-based breast cancer diagnosis. Firstly, four breast cancer imaging modalities, namely mammography, ultrasonography, magnetic resonance imaging and positron emission tomography, are outlined and briefly compared, and the public datasets corresponding to multiple imaging modalities are listed. Focusing on the different tasks (lesion detection, segmentation and classification) of deep learning architectures based on the above four different imaging modalities, a systematic review of the algorithms is conducted, and the performance of each algorithm, improvement ideas, and their advantages and disadvantages are compared and analyzed. Finally, the problems of the existing techniques are analyzed and the future development direction is prospected with respect to the limitations of the current work.

Key words: breast cancer, deep learning, computer-aided diagnosis, imaging examination

摘要： 乳腺癌是女性最常见的恶性肿瘤，其早期发现具有决定性意义。乳腺影像学检查在早期发现乳腺癌以及在治疗期间监测与评估方面发挥着重要作用，但人工检测医学影像通常耗时耗力。最近，深度学习算法在早期乳腺癌诊断工作中取得了显著进展。通过梳理近几年的相关文献，对深度学习技术在不同成像模式的乳腺癌诊断中的应用进行了系统综述，旨在为深入开展基于深度学习的乳腺癌诊断研究提供参考。首先概述了乳腺X线摄影、超声影像、磁共振成像和正电子发射计算机断层显像四种乳腺癌成像模式并进行了简要对比，列举了多种成像方式对应的公共数据集。重点对基于上述四种不同成像模式的深度学习架构的不同任务（病变检测、分割和分类）进行了系统的综述，对比分析了各算法性能、改进思路及其优缺点。最后，对现有技术存在的问题进行分析，并针对目前工作的局限性对未来发展方向进行展望。

关键词: 乳腺癌, 深度学习, 计算机辅助诊断, 影像学检查