DeepSurvNet: deep survival convolutional network for brain cancer survival rate classification based on histopathological images.

ABSTRACT: Histopathological whole slide images of haematoxylin and eosin (H&E)-stained biopsies contain valuable information with relation to cancer disease and its clinical outcomes. Still, there are no highly accurate automated methods to correlate histolopathological images with brain cancer patients' survival, which can help in scheduling patients therapeutic treatment and allocate time for preclinical studies to guide personalized treatments. We now propose a new classifier, namely, DeepSurvNet powered by deep convolutional neural networks, to accurately classify in 4 classes brain cancer patients' survival rate based on histopathological images (class I, 0-6 months; class II, 6-12 months; class III, 12-24 months; and class IV, >24 months survival after diagnosis). After training and testing of

SUBMITTER: Zadeh Shirazi A 

PROVIDER: S-EPMC7188709 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature