Prognostic analysis of histopathological images using pre-trained convolutional neural networks: application to hepatocellular carcinoma.

ABSTRACT: Histopathological images contain rich phenotypic descriptions of the molecular processes underlying disease progression. Convolutional neural networks, state-of-the-art image analysis techniques in computer vision, automatically learn representative features from such images which can be useful for disease diagnosis, prognosis, and subtyping. Hepatocellular carcinoma (HCC) is the sixth most common type of primary liver malignancy. Despite the high mortality rate of HCC, little previous work has made use of CNN models to explore the use of histopathological images for prognosis and clinical survival prediction of HCC. We applied three pre-trained CNN models-VGG 16, Inception V3 and ResNet 50-to extract features from HCC histopathological images. Sample visualization and classification analy

SUBMITTER: Lu L 

PROVIDER: S-EPMC7073245 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature