Intrinsic subtype classification of breast lesions on mammograms by contrastive learning

Chisako Muramatsu; Mikinao Oiwa; Tomonori Kawasaki; Hiroshi Fujita

doi:10.1117/12.2613173

4 April 2022 Intrinsic subtype classification of breast lesions on mammograms by contrastive learning

Chisako Muramatsu, Mikinao Oiwa, Tomonori Kawasaki, Hiroshi Fujita

Proceedings Volume 12033, Medical Imaging 2022: Computer-Aided Diagnosis; 120331S (2022) https://doi.org/10.1117/12.2613173
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

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.

Conference Presentation

Citation Download Citation

Chisako Muramatsu, Mikinao Oiwa, Tomonori Kawasaki, and Hiroshi Fujita "Intrinsic subtype classification of breast lesions on mammograms by contrastive learning", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 120331S (4 April 2022); https://doi.org/10.1117/12.2613173

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available