Detection of liver cirrhosis in standard T2-weighted MRI using deep transfer learning.

ABSTRACT:

Objectives

To investigate the diagnostic performance of deep transfer learning (DTL) to detect liver cirrhosis from clinical MRI.

Methods

The dataset for this retrospective analysis consisted of 713 (343 female) patients who underwent liver MRI between 2017 and 2019. In total, 553 of these subjects had a confirmed diagnosis of liver cirrhosis, while the remainder had no history of liver disease. T2-weighted MRI slices at the level of the caudate lobe were manually exported for DTL analysis. Data were randomly split into training, validation, and test sets (70%/15%/15%). A ResNet50 convolutional neural network (CNN) pre-trained on the ImageNet archive was used for cirrhosis detection with and without upstream liver segmentation. Classification performance for detection of liv

SUBMITTER: Nowak S 

PROVIDER: S-EPMC8523404 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature