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

Imaging Informatics (318) Machine Learning (2173) Medical imaging (123) Clinical Information and Decision Making (2007) Decision Support for Health Professionals (1594) Clinical Informatics (1381) Gastroenterology and Hepatology (140)

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 (3) Tweetations Metrics

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  3. Liu Y, Yin H, Zheng Z, Liu W, Zhang T, Cai L, Niu H, Lv H, Yang Z, Wang Z, Ren P. Two-Minute Deep Learning–Powered Brain Quantitative Mapping: Accelerating Clinical Imaging With Synthetic Magnetic Resonance Imaging. JMIR Medical Informatics 2026;14:e79389 View