Pan-Renal Cell Carcinoma classification and survival prediction from histopathology images using deep learning.

ABSTRACT: Histopathological images contain morphological markers of disease progression that have diagnostic and predictive values. In this study, we demonstrate how deep learning framework can be used for an automatic classification of Renal Cell Carcinoma (RCC) subtypes, and for identification of features that predict survival outcome from digital histopathological images. Convolutional neural networks (CNN's) trained on whole-slide images distinguish clear cell and chromophobe RCC from normal tissue with a classification accuracy of 93.39% and 87.34%, respectively. Further, a CNN trained to distinguish clear cell, chromophobe and papillary RCC achieves a classification accuracy of 94.07%. Here, we introduced a novel support vector machine-based method that helped to break the multi-class classifi

SUBMITTER: Tabibu S 

PROVIDER: S-EPMC6642160 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature