Deep learning for classification of breast cancer in optical coherence tomography (OCT) imaging (Conference Presentation)

Rohan Bareja; Diana Mojahed; Christine P. Hendon

doi:10.1117/12.2546256

9 March 2020 Deep learning for classification of breast cancer in optical coherence tomography (OCT) imaging (Conference Presentation)

Rohan Bareja, Diana Mojahed, Christine P. Hendon

Author Affiliations +

Proceedings Volume 11229, Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XVIII; 112290F (2020) https://doi.org/10.1117/12.2546256
Event: SPIE BiOS, 2020, San Francisco, California, United States

Abstract

Optical coherence tomography (OCT) is being investigated as an intraoperative margin assessment tool for breast cancer. In this work, we developed a customized deep convolutional neural network (CNN) for classification of breast cancer in OCT images. Images were acquired with a custom ultrahigh-resolution OCT system and a standard resolution system. We classify healthy tissues such as stroma and adipose tissue, as well as diseased tissue including ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC). Future work involves increasing representation from different kinds of tumors such as mucinous carcinoma, papillary carcinoma, and phyllodes tumors.

Conference Presentation

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Rohan Bareja, Diana Mojahed, and Christine P. Hendon "Deep learning for classification of breast cancer in optical coherence tomography (OCT) imaging (Conference Presentation)", Proc. SPIE 11229, Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XVIII, 112290F (9 March 2020); https://doi.org/10.1117/12.2546256

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