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

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.