Self-adversarial Learning for Detection of Clustered Microcalcifications in Mammograms

Author:

Ouyang Xi,Che Jifei,Chen Qitian,Li Zheren,Zhan Yiqiang,Xue Zhong,Wang Qian,Cheng Jie-Zhi,Shen Dinggang

Publisher

Springer International Publishing

Reference22 articles.

1. AlGhamdi, M., Abdel-Mottaleb, M., Collado-Mesa, F.: DU-Net: convolutional network for the detection of arterial calcifications in mammograms. IEEE Trans. Med. Imaging 39(10), 3240–3249 (2020)

2. Basile, T., et al.: Microcalcification detection in full-field digital mammograms: a fully automated computer-aided system. Physica Medica 64, 1–9 (2019)

3. Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre, L.A., Jemal, A.: Global cancer statistics 2018: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: Cancer J. Clin. 68(6), 394–424 (2018)

4. Bria, A., Karssemeijer, N., Tortorella, F.: Learning from unbalanced data: a cascade-based approach for detecting clustered microcalcifications. Med. Image Anal. 18(2), 241–252 (2014)

5. Cai, G., Guo, Y., Zhang, Y., Qin, G., Zhou, Y., Lu, Y.: A fully automatic microcalcification detection approach based on deep convolution neural network. In: Medical Imaging 2018: Computer-Aided Diagnosis, vol. 10575, p. 105752Q. International Society for Optics and Photonics (2018)

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