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Proceedings Paper

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

Paper 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.

Paper Details

Date Published: 9 March 2020
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Proc. SPIE 11229, Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XVIII, 112290F (9 March 2020); doi: 10.1117/12.2546256
Show Author Affiliations
Rohan Bareja, Columbia Univ. (United States)
Diana Mojahed, Columbia Univ. (United States)
Christine P. Hendon, Columbia Univ. (United States)


Published in SPIE Proceedings Vol. 11229:
Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XVIII
Anita Mahadevan-Jansen, Editor(s)

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