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.

, , , , ,
doi.org/10.1117/1.JBO.23.6.066002
Journal of Biomedical Optics
Department of Physics

Huttunen, M.J. (Mikko J.), Hassan, A. (Abdurahman), McCloskey, C.W. (Curtis W.), Fasih, S. (Sijyl), Upham, J. (Jeremy), Vanderhyden, B.C. (Barbara C.), … Murugkar, S. (2018). Automated classification of multiphoton microscopy images of ovarian tissue using deep learning. Journal of Biomedical Optics, 23(6). doi:10.1117/1.JBO.23.6.066002