Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI.

ABSTRACT:

Objectives

To develop and validate a proof-of-concept convolutional neural network (CNN)-based deep learning system (DLS) that classifies common hepatic lesions on multi-phasic MRI.

Methods

A custom CNN was engineered by iteratively optimizing the network architecture and training cases, finally consisting of three convolutional layers with associated rectified linear units, two maximum pooling layers, and two fully connected layers. Four hundred ninety-four hepatic lesions with typical imaging features from six categories were utilized, divided into training (n = 434) and test (n = 60) sets. Established augmentation techniques were used to generate 43,400 training samples. An Adam optimizer was used for training. Monte Carlo cross-validation was performed. After model engin

SUBMITTER: Hamm CA 

PROVIDER: S-EPMC7251621 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature