Intrinsic subtype classification of breast lesions on mammograms by contrastive learning
Chisako Muramatsu, Mikinao Oiwa, Tomonori Kawasaki, Hiroshi Fujita · Medical Imaging 2022: Computer-Aided Diagnosis · 2022
Periodic breast cancer screening with mammography is considered effective in decreasing breast cancer mortality. Once cancer is found, the best treatment is selected based on the characteristic of cancer. In this study, we investigated a method to classify breast cancer lesions into four molecular subtypes to assist diagnosis and treatment planning. Because of a limited number of samples and imbalanced types, the lesions were classified based on the similarities of samples using a contrastive learning. The convolutional neural network (CNN) was trained by self-supervised method using paired views of the same lesions with contrastive loss. The subtype was determined by k-nearest neighbor classifier using deep features obtained by the trained network. The proposed model was tested using 385 cases by a 4-fold cross validation. The results are compared with CNN models without and with pretraining. The result indicates the potential usefulness of the proposed method. The computerized subtype classification may support a prompt treatment planning and proper patient care.