Deep learning enables automated scoring of liver fibrosis stages.

ABSTRACT: Current liver fibrosis scoring by computer-assisted image analytics is not fully automated as it requires manual preprocessing (segmentation and feature extraction) typically based on domain knowledge in liver pathology. Deep learning-based algorithms can potentially classify these images without the need for preprocessing through learning from a large dataset of images. We investigated the performance of classification models built using a deep learning-based algorithm pre-trained using multiple sources of images to score liver fibrosis and compared them against conventional non-deep learning-based algorithms - artificial neural networks (ANN), multinomial logistic regression (MLR), support vector machines (SVM) and random forests (RF). Automated feature classification and fibrosis scorin

SUBMITTER: Yu Y 

PROVIDER: S-EPMC6207665 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature