[1] 马丹丹, 刘坤, 齐晓伟. 2018年全球癌症统计: 乳腺癌发病和死亡人数统计[J]. 中华乳腺病杂志(电子版), 2018, 12(6): 375.

    Ma D D, Liu K, Qi X W. Global cancer statistics in 2018: statistics on morbidity and mortality of breast cancer[J]. Chinese Journal of Breast Disease (Electronic Edition), 2018, 12(6): 375.

[2] 何明艳. 乳腺癌流行趋势分析与术后放疗效应研究[D]. 衡阳: 南华大学, 2019: 20- 38.

    He MY. Analysis of epidemic trend with breast cancer and research on the effect of postoperative radiotherapy[D]. Hengyang: University of South China, 2019: 20- 38.

[3] 周星彤, 沈松杰, 孙强. 中国乳腺癌筛查现状及进展[J]. 中国医学前沿杂志(电子版), 2020, 12(3): 6-11.

    Zhou X T, Shen S J, Sun Q. Current situation and progress of breast cancer screening in China[J]. Chinese Journal of the Frontiers of Medical Science (Electronic Version), 2020, 12(3): 6-11.

[4] Machida Y, Saita A, Namba H, et al. Automated volumetric breast density estimation out of digital breast tomosynthesis data: feasibility study of a new software version[J]. Springerplus, 2016, 5(1): 780-787.

[5] Rouhi R, Jafari M. Classification of benign and malignant breast tumors based on hybrid level set segmentation[J]. Expert Systems With Applications, 2016, 46: 45-59.

[6] Li C X, Shen C B, Xue K, et al. Artificial intelligence in dermatology: past, present, and future[J]. Chinese Medical Journal, 2019, 132(17): 2017-2020.

[7] Medjahed S A, Saadi T A, Benyettou A. Breast cancer diagnosis by using k-nearest neighbor with different distances and classification rules[J]. International Journal of Computer Applications, 2013, 62(1): 1-5.

[8] 鲁逸峰, 金琴花, 荆晶, 等. 基于机器学习的可降解支架检测与分割算法[J]. 光学学报, 2018, 38(2): 0215005.

    Lu Y F, Jin Q H, Jing J, et al. Detection and segmentation algorithm for bioresorbable vascular scaffolds struts based on machine learning[J]. Acta Optica Sinica, 2018, 38(2): 0215005.

[9] Liu N, Shen J, Xu M, et al. Improved cost-sensitive support vector machine classifier for breast cancer diagnosis[J]. Mathematical Problems in Engineering, 2018, 2018: 1-13.

[10] RenR, HeK, GirshickR, et al. Faster R-CNN: towards real-time object detection with region proposal networks[C]∥ Proceedings of the 28th International Conference on Neural Information Processing Systems. [S.l.:s.n.], 2015: 91- 99.

[11] He K M, Gkioxari G, Dollár P, et al. Mask R-CNN[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(2): 386-397.

[12] Zhang Z, Liang X, Dong X, et al. A sparse-view CT reconstruction method based on combination of DenseNet and deconvolution[J]. IEEE Transactions on Medical Imaging, 2018, 37(6): 1407-1417.

[13] 黄鸿, 彭超, 吴若愚, 等. 基于部分注释CT图像的自监督迁移学习肺结节分类[J]. 光学学报, 2020, 40(18): 1810003.

    Huang H, Peng C, Wu R Y, et al. Self-supervised transfer learning of pulmonary nodule classification based on partially annotated CT images[J]. Acta Optica Sinica, 2020, 40(18): 1810003.

[14] 杨其利, 周炳红, 郑伟, 等. 基于全卷积递归网络的弱小目标检测方法[J]. 光学学报, 2020, 40(13): 1310002.

    Yang Q L, Zhou B H, Zheng W, et al. Dim and small target detection based on fully convolutional recursive network[J]. Acta Optica Sinica, 2020, 40(13): 1310002.

[15] Jain A L, Mullins J, Smith J R, et al. Unusual recurrent metastasizing benign breast papilloma: a case report[J]. Journal of Medical Case Reports, 2020, 14(1): 1752-1947.

[16] 文婵娟. 数字乳腺断层摄影(DBT)对乳腺病变诊断价值的初步研究[D]. 广州: 南方医科大学, 2014: 20- 30.

    Wen CJ. The diagnostic performance of digital breast tomosynthesis for breast lesions: a preliminary study[D]. Guangzhou: Southern Medical University, 2014: 20- 30.

[17] Ma J Y, Ma Y, Li C. Infrared and visible image fusion methods and applications: a survey[J]. Information Fusion, 2019, 45: 153-178.

[18] Li H, Zhuang S S, Li D A, et al. Benign and malignant classification of mammogram images based on deep learning[J]. Biomedical Signal Processing and Control, 2019, 51: 347-354.

[19] Shafiee MJ, ChywlB, LiF, et al. Fast YOLO: a fast You Only Look Once system for real-time embedded object detection in video[EB/OL]. [2020-07-13].https:∥arxiv.org/abs/1709. 05943.

[20] LiuW, AnguelovD, ErhanD, et al.SSD: single shot MultiBox detector[C]∥European Conference on Computer Vision. [S.l.]: Springer International Publishing, 2016: 21- 37.

[21] 张景祥, 王士同, 邓赵红, 等. 融合异构特征的子空间迁移学习算法[J]. 自动化学报, 2014, 40(2): 236-246.

    Zhang J X, Wang S T, Deng Z H, et al. A subspace transfer learning algorithm integrating heterogeneous features[J]. Acta Automatica Sinica, 2014, 40(2): 236-246.