Automated classification of multiphoton microscopy images of ovarian tissue using deep learning
Research output: Contribution to journal › Article › Scientific › peer-review
|Journal||JOURNAL OF BIOMEDICAL OPTICS|
|Publication status||Published - 13 Jun 2018|
|Publication type||A1 Journal article-refereed|
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 fluo- rescence 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.