Mass Classification of Breast Mammogram Based on Attention Mechanism and Transfer Learning

Wenhui Xu; Yijian Pei; Donglin Gao; Jiude Zhu; Yunkai Liu

doi:10.3788/LOP202158.0410007

[1] World Health Organization[2020-06-03]. Breast cancer [2020-06-03].https:∥www.who.int/cancer/prevention/diagnosisscreening/breast-cancer/en/..

[2] Yin J H, Zhang Y, Lai J G et al. Global burden of disease cancer collaboration[J]. JAMA Oncology, 1, 505-527(2015).

[3] Shi J, Liang D, Li D J et al. Epidemiological status of global female breast cancer[J]. China Cancer, 26, 683-690(2017).

[4] Heywang-Köbrunner S H, Hacker A, Sedlacek S. Advantages and disadvantages of mammography screening[J]. Breast Care (Basel, Switzerland), 6, 199-207(2011). http://pubmedcentralcanada.ca/pmcc/articles/PMC3132967/

[5] 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, 51, 347-354(2019). http://www.sciencedirect.com/science/article/pii/S1746809419300618

[6] Kooi T. Litjens G, van Ginneken B, et al. Large scale deep learning for computer aided detection of mammographic lesions[J]. Medical Image Analysis, 35, 303-312(2017).

[7] Huang Y J, Shi Z F, Wang Z Q. An improved U-Net based on mixed loss function for segmentation method of liver medical image[J]. Laser & Optoelectronics Progress, 57, 221003(2020).

[8] Zheng B, Yang C, Ma X P et al. Malignant thyroid nodule detection based on circular convolutional neural network[J]. Laser & Optoelectronics Progress, 56, 241003(2019).

[9] Ai L M, Shi K Z. Low-grade gliomas MR images segmentation based on conditional generative adversarial networks[J]. Laser & Optoelectronics Progress, 57, 221004(2020).

[10] Wang P, Xiao X. Glissen Brown J R, et al. Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy[J]. Nature Biomedical Engineering, 2, 741-748(2018). http://www.nature.com/articles/s41551-018-0301-3

[11] Falconí L G, Pérez M, Aguilar W G. Transfer learning in breast mammogram abnormalities classification with mobilenet and nasnet[C]∥2019 International Conference on Systems, Signals and Image Processing (IWSSIP), June 5-7, 2019, Osijek,, 109-114(2019).

[12] Sun L L, Xu Y. Research on classification method of mammography based on deep learning[J]. Computer Engineering and Applications, 54, 13-19(2018).

[13] Yang J, Hu M D, Li L, Applications: 1-8 [2020-06-04]. http:∥kns.cnki.net/kcms/detail/11 et al. TP. 20191205.1030.002.html.(2127).

[14] Khan H N, Shahid A R, Raza B et al. Multi-view feature fusion based four views model for mammogram classification using convolutional neural network[J]. IEEE Access, 7, 165724-165733(2019). http://www.researchgate.net/publication/337234099_Multi-View_Feature_Fusion_based_Four_Views_Model_for_Mammogram_Classification_using_Convolutional_Neural_Network

[15] Li H Y, Chen D D, Nailon W H et al. A deep dual-path network for improved mammogram image processing[C]∥2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 12-17, 2019, Brighton, United Kingdom., 1224-1228(2019).

[16] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA., 770-778(2016).

[17] Woo S, Park J, Lee J Y et al[M]. CBAM: convolutional block attention module, 3-19(2018).

[18] DengJ, DongW, SocherR, et al.ImageNet: a large-scale hierarchical image database[C]∥2009 IEEE Conference on Computer Vision and Pattern Recognition, June 20-25, 2009, Miami, FL, USA. New York: IEEE Press, 2009: 248- 255.

[19] Lee R S, Gimenez F, Hoogi A et al. A curated mammography data set for use in computer-aided detection and diagnosis research[J]. Scientific Data, 4, 170177(2017). http://europepmc.org/articles/PMC5735920/

[20] Wu N, Phang J, Park J et al. Deepneural networks improve radiologists’ performance in breast cancer screening[J]. IEEE Transactions on Medical Imaging, 39, 1184-1194(2019). http://arxiv.org/abs/1903.08297

[21] Simonyan K. -09-04)[2020-06-04]. https:∥arxiv., org/abs/1409, 1556(2014).

[22] Szegedy C, Vanhoucke V, Ioffe S et al. Rethinking the inception architecture for computer vision[C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA., 2818-2826(2016).

[23] Selvaraju R R, Cogswell M, Das A et al. Grad-CAM: visual explanations from deep networks via gradient-based localization[J]. International Journal of Computer Vision, 128, 336-359(2020).