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

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