Deep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging features.

ABSTRACT:

Objectives

To develop a proof-of-concept "interpretable" deep learning prototype that justifies aspects of its predictions from a pre-trained hepatic lesion classifier.

Methods

A convolutional neural network (CNN) was engineered and trained to classify six hepatic tumor entities using 494 lesions on multi-phasic MRI, described in Part 1. A subset of each lesion class was labeled with up to four key imaging features per lesion. A post hoc algorithm inferred the presence of these features in a test set of 60 lesions by analyzing activation patterns of the pre-trained CNN model. Feature maps were generated that highlight regions in the original image that correspond to particular features. Additionally, relevance scores were assigned to each identified feature, denoting the rel

SUBMITTER: Wang CJ 

PROVIDER: S-EPMC7243989 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature