Breast mass classification based on supervised contrastive learning and multi‐view consistency penalty on mammography. Issue 6 (12th May 2022)

• Record Type: Journal Article
• Authors: Sun, Lilei; Wen, Jie; Wang, Junqian; Zhang, Zheng; Zhao, Yong; Zhang, Guiying; Xu, Yong
• Other Names: Rida Imad guestEditor.; Marcialis Gian Luca guestEditor.; Fei Lunke guestEditor.; Istrate Dan guestEditor.; Fierrez Julian guestEditor.
• Is Part Of: IET biometrics. Volume 11:Issue 6(2022)
• Page Start: 588
• Page End: 600
• Publication Date: 2022-05-12
• Subjects: Biometric identification -- Periodicals; 570.15195
• DOI: 10.1049/bme2.12076
• Languages: English
• ISSNs: 2047-4938

Abstract

Abstract: Breast cancer accounts for the largest number of patients among all cancers in the world. Intervention treatment for early breast cancer can dramatically extend a woman's 5‐year survival rate. However, the lack of public available breast mammography databases in the field of Computer‐aided Diagnosis and the insufficient feature extraction ability from breast mammography limit the diagnostic performance of breast cancer. In this paper, A novel classification algorithm based on Convolutional Neural Network (CNN) is proposed to improve the diagnostic performance for breast cancer on mammography. A multi‐view network is designed to extract the complementary information between the Craniocaudal (CC) and Mediolateral Oblique (MLO) mammographic views of a breast mass. For the different predictions of the features extracted from the CC view and MLO view of the same breast mass, the proposed algorithm forces the network to extract the consistent features from the two views by the cross‐entropy function with an added consistent penalty term. To exploit the discriminative features from the insufficient mammographic images, the authors learnt an encoder in the classification model to learn the invariable representations from the mammographic breast mass by Supervised Contrastive Learning (SCL) to weaken the side effect of colour jitter and illumination of mammographic breast mass on image quality degradation. The experimental results of all the classification algorithms mentioned in this paper on Digital Database for Screening Mammography (DDSM) illustrate that the proposed algorithm greatly improves the classification performance and diagnostic speed of mammographic breast mass, which is of great significance for breast cancer diagnosis.