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

Artificial Intelligence (1768) Clinical Informatics (1175) Clinical Information and Decision Making (1683) Imaging Informatics (238) Machine Learning (1764) Advanced Data Analytics in eHealth (169)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/44119, first published .
Performance Test of a Well-Trained Model for Meningioma Segmentation in Health Care Centers: Secondary Analysis Based on Four Retrospective Multicenter Data Sets

Performance Test of a Well-Trained Model for Meningioma Segmentation in Health Care Centers: Secondary Analysis Based on Four Retrospective Multicenter Data Sets

Performance Test of a Well-Trained Model for Meningioma Segmentation in Health Care Centers: Secondary Analysis Based on Four Retrospective Multicenter Data Sets

Authors of this article:

Chaoyue Chen1 Author Orcid Image ;   Yuen Teng1 Author Orcid Image ;   Shuo Tan2 Author Orcid Image ;   Zizhou Wang2, 3 Author Orcid Image ;   Lei Zhang2 Author Orcid Image ;   Jianguo Xu1 Author Orcid Image
Article Authors Cited by (2) Tweetations (4) Metrics

Journals

  1. Wang T, Hong J, Lee W, Lin Y, Yang H, Lee C, Chen H, Wu H, You W, Wu Y. Performance of Convolutional Neural Network Models in Meningioma Segmentation in Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis. Neuroinformatics 2024;23(1) View
  2. Chen C, Zhao Y, Cai L, Jiang H, Teng Y, Zhang Y, Zhang S, Zheng J, Zhao F, Huang Z, Xu X, Zan X, Xu J, Zhang L, Xu J. A multi-modal deep learning model for prediction of Ki-67 for meningiomas using pretreatment MR images. npj Precision Oncology 2025;9(1) View