Intrinsic subtype classification of breast cancers on mammograms using local selective patches

Chisako Muramatsu; Mikinao Oiwa; Tomonori Kawasaki; Rieko Nishimura; Hiroshi Fujita

doi:10.1117/12.3008738

3 April 2024 Intrinsic subtype classification of breast cancers on mammograms using local selective patches

Chisako Muramatsu, Mikinao Oiwa, Tomonori Kawasaki, Rieko Nishimura, Hiroshi Fujita

Author Affiliations +

Proceedings Volume 12927, Medical Imaging 2024: Computer-Aided Diagnosis; 1292723 (2024) https://doi.org/10.1117/12.3008738
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

Periodic breast cancer screening with mammography is considered effective in decreasing breast cancer mortality. When cancer is found, the best treatment method is selected considering the cancer subtypes. In this study, we investigated a method to distinguish breast cancers with poor prognosis from those with relatively good prognosis to assist diagnosis and treatment planning. In our previous study, all regions of interest including cancer lesions were resized to the same matrix size, which had caused loss of size and local characteristic information of the lesions. In this study, local patches with the original pixel size were automatically selected during the training in each epoch. The patch sampling could also reduce the effect of class imbalance. The proposed model was tested using 264 cases by a 4-fold cross validation. The result indicates the potential usefulness of the proposed method. The computerized subtype classification may support a prompt treatment planning and proper patient care.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chisako Muramatsu, Mikinao Oiwa, Tomonori Kawasaki, Rieko Nishimura, and Hiroshi Fujita "Intrinsic subtype classification of breast cancers on mammograms using local selective patches", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 1292723 (3 April 2024); https://doi.org/10.1117/12.3008738

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