Accessibility settings

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/80351, first published .
Medical professional assisting patient with headphones in MRI machine

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

Journals

  1. Xu J. Factors Influencing Stroke Severity Based on Collateral Circulation, Clinical Markers and Machine Learning. Diagnostics 2025;15(23):2983 View
  2. 谭 泽. Research Progress on Risk Factors and Prediction Models of Hepatitis C-Related Liver Cirrhosis. Advances in Clinical Medicine 2026;16(01):2024 View
  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
  4. CHANG W, KUO C, XU J, SHIH H. GENERALIZATION PERFORMANCE OF DEEP LEARNING MODELS FOR SKIN LESION CLASSIFICATION. Journal of Mechanics in Medicine and Biology 2026 View
  5. Akıneden A, Abri R, Akıllı F, Abri S, Türkel S, Akıllı B, Avuçlu E, Duman Y. Deep learning for automated classification of antinuclear antibody patterns on HEp-2 indirect immunofluorescence images. Immunologic Research 2026;74(1) View