Multiparametric MRI-Based Interpretable Radiomics Machine Learning Model Differentiates Medulloblastoma and Ependymoma in Children: A Two-Center Study

Yimit, Yasen; Yasin, Parhat; Tuersun, Abudouresuli; Wang, Jingru; Wang, Xiaohong; Huang, Chencui; Abudoubari, Saimaitikari; Chen, Xingzhi; Ibrahim, Irshat; Nijiati, Pahatijiang; Wang, Yunling; Zou, Xiaoguang; Nijiati, Mayidili

doi:10.1016/j.acra.2024.02.040

Academic Radiology

2024

DOI: 10.1016/j.acra.2024.02.040

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Multiparametric MRI-Based Interpretable Radiomics Machine Learning Model Differentiates Medulloblastoma and Ependymoma in Children: A Two-Center Study

Yasen Yimit,

Parhat Yasin,

Abudouresuli Tuersun

et al.

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Cited by 2 publications

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Multiparametric MRI-Based Interpretable Radiomics Machine Learning Model Differentiates Medulloblastoma and Ependymoma in Children: A Two-Center Study

Cited by 2 publications

References 39 publications

Development and Validation of Multiparametric MRI-based Interpretable Deep Learning Radiomics Fusion Model for Predicting Lymph Node Metastasis and Prognosis in Rectal Cancer: A Two-center Study

Development and Validation of Multiparametric MRI-based Interpretable Deep Learning Radiomics Fusion Model for Predicting Lymph Node Metastasis and Prognosis in Rectal Cancer: A Two-center Study

ViT-CB: Integrating hybrid Vision Transformer and CatBoost to enhanced brain tumor detection with SHAP

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