Generalizable convolutional neural network (CNN) classification of breast cancer using images from two optical coherence tomography (OCT) systems

Diana Mojahed; Rohan Bareja; Theresa Lye; Richard Ha; Hanina Hibshoosh; Christine Hendon

doi:10.1117/12.2577422

5 March 2021 Generalizable convolutional neural network (CNN) classification of breast cancer using images from two optical coherence tomography (OCT) systems

Diana Mojahed, Rohan Bareja, Theresa Lye, Richard Ha, Hanina Hibshoosh, Christine Hendon

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Proceedings Volume 11631, Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XIX; 116310J (2021) https://doi.org/10.1117/12.2577422
Event: SPIE BiOS, 2021, Online Only

Abstract

The purpose of this study was to develop a generalizable convolutional neural network (CNN) classification technique of optical coherence tomography (OCT) images of breast tissue acquired from multiple OCT systems. We imaged lumpectomy and mastectomy specimens (acquired through the Columbia University Tissue Bank) from 31 patients. In our early results, we classified the images into healthy tissue (adipose and stroma) and diseased, which included ductal carcinoma in situ (DCIS), mucinous carcinoma, and invasive ductal carcinoma (IDC). Our goal is to expand our classification to differentiate the diseased tissue into subclasses of DCIS, IDC, mucinous carcinoma, and benign tissue.

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Diana Mojahed, Rohan Bareja, Theresa Lye, Richard Ha, Hanina Hibshoosh, and Christine Hendon "Generalizable convolutional neural network (CNN) classification of breast cancer using images from two optical coherence tomography (OCT) systems", Proc. SPIE 11631, Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XIX, 116310J (5 March 2021); https://doi.org/10.1117/12.2577422

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KEYWORDS

Optical coherence tomography

Image resolution

Classification systems

Convolutional neural networks

Image classification

Breast cancer

Tissues

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