This paper is in the following e-collection/theme issue:

Imaging Informatics (292) Machine Learning (2018) Medical imaging (102) Clinical Information and Decision Making (1860) Decision Support for Health Professionals (1500) Clinical Informatics (1286) Gastroenterology and Hepatology (111)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/80351, first published .
Rapid Liver Fibrosis Evaluation Using the UNet-ResNet50-32 × 4d Model in Magnetic Resonance Elastography: Retrospective Study

Rapid Liver Fibrosis Evaluation Using the UNet-ResNet50-32 × 4d Model in Magnetic Resonance Elastography: Retrospective Study

Rapid Liver Fibrosis Evaluation Using the UNet-ResNet50-32 × 4d Model in Magnetic Resonance Elastography: Retrospective Study

Authors of this article:

Pei-Yuan Su1, 2 Author Orcid Image ;   Han-Jie Shih3 Author Orcid Image ;   Jia-Lang Xu4 Author Orcid Image
Article Authors Cited by Tweetations Metrics

Pei-Yuan Su   1, 2 , MD ;   Han-Jie Shih   3 , BS ;   Jia-Lang Xu   4 , PhD

1 Department of Internal Medicine, Division of Gastroenterology, Changhua Christian Hospital, Changhua, Taiwan

2 Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan

3 Department of Medical Imaging, Changhua Christian Hospital, Changhua, Taiwan

4 Department of Applied Statistics, National Taichung University of Science and Technology, Taichung, Taiwan

Corresponding Author:

  • Jia-Lang Xu, PhD
  • Department of Applied Statistics
  • National Taichung University of Science and Technology
  • No. 129, Section 3, Sanmin Road, North District
  • Taichung 404336
  • Taiwan
  • Phone: 886 4-2219-6076
  • Fax: 886 4-2219-6330
  • Email: jlxu.academy@gmail.com