Breast cancer magnification-independent multi-class histopathology classification using dual-step model

Nathan Lang; Devansh Saxena; Tina Yen; Julie Jorns; Bing Yu; Dong Hye Ye

doi:10.1117/12.2582299

15 February 2021 Breast cancer magnification-independent multi-class histopathology classification using dual-step model

Nathan Lang, Devansh Saxena, Tina Yen, Julie Jorns, Bing Yu, Dong Hye Ye

Author Affiliations +

Proceedings Volume 11603, Medical Imaging 2021: Digital Pathology; 1160311 (2021) https://doi.org/10.1117/12.2582299
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

Computer-aided classification of breast cancer using histopathological images can play a significant role in clinical practice by detecting the distinct type of malignant and/or benign tumor. However, currently proposed deep learning models developed using the BreakHis dataset only conduct a binary classification between benign and malignant tumors, and are also scale-dependent. This study utilizes a ResNet-50 implementation to transform images from the four magnification factors such that all images can be used for training the deep neural network. This process yields a larger training set that is also scale-independent. For this paper, we utilized a dual step approach with the first pass being binary classification and the second pass being a multi-class classifier of malignant tumors that offers higher clinical utility.

Conference Presentation

Citation Download Citation

Nathan Lang, Devansh Saxena, Tina Yen, Julie Jorns, Bing Yu, and Dong Hye Ye "Breast cancer magnification-independent multi-class histopathology classification using dual-step model", Proc. SPIE 11603, Medical Imaging 2021: Digital Pathology, 1160311 (15 February 2021); https://doi.org/10.1117/12.2582299

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