Scientific Publications Database

Article Title: Automated classification of multiphoton microscopy images of ovarian tissue using deep learning
Authors: Huttunen, Mikko J.; Hassan, Abdurahman; McCloskey, Curtis W.; Fasih, Sijyl; Upham, Jeremy; Vanderhyden, Barbara C.; Boyd, Robert W.; Murugkar, Sangeeta
Journal: JOURNAL OF BIOMEDICAL OPTICS Volume 23 Issue 6
Date of Publication:2018
Abstract:
Histopathological image analysis of stained tissue slides is routinely used in tumor detection and classification. However, diagnosis requires a highly trained pathologist and can thus be time-consuming, labor-intensive, and potentially risk bias. Here, we demonstrate a potential complementary approach for diagnosis. We show that multiphoton microscopy images from unstained, reproductive tissues can be robustly classified using deep learning techniques. We fine-train four pretrained convolutional neural networks using over 200 murine tissue images based on combined second-harmonic generation and two-photon excitation fluorescence contrast, to classify the tissues either as healthy or associated with high-grade serous carcinoma with over 95% sensitivity and 97% specificity. Our approach shows promise for applications involving automated disease diagnosis. It could also be readily applied to other tissues, diseases, and related classification problems. (C) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE).