Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
307-02-002 ISMRM Abstract

Explainable Federated Multimodal MRI 3D-CNN for Predicting IDH Mutation Status in Brain Gliomas: A Multicenter Study

Primary: Neuro - Tumors
Secondary: Analysis Methods - Radiomics
307-02-002 · Advances in MRI Radiomics for Disease Characterization and Outcome Prediction · Monday, 11 May, 8:20 AM–10:10 AM · Meeting Room 1.40
Keywords: Glioma IDH mutation MRI Deep learning interpretability Federated Learning
Accepted
Ao Liu 1, Xiuzheng Yue2, Hao Yu3
1radiology department, Affiliated Hospital of Jining Medical College, jining, China
2Clinical and Technical Support, Philips Healthcare (Beijing), Beijing, China
3Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Jining, China
Presenting Author: Ao Liu

Synopsis

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References

1. Dayan I, Roth HR, Zhong A, et al. Federated learning for predicting clinical outcomes in patients with COVID-19. Nat Med. 2021;27(10):1735-1743. doi:10.1038/s41591-021-01506-3 [doi]
2. Schmidt K, Bearce B, Chang K, et al. Fair evaluation of federated learning algorithms for automated breast density classification: The results of the 2022 ACR-NCI-NVIDIA federated learning challenge. Med Image Anal. 2024;95:103206. doi:10.1016/j.media.2024.103206 [doi]
3. Albalawi E, T R M, Thakur A, et al. Integrated approach of federated learning with transfer learning for classification and diagnosis of brain tumor. BMC Med Imaging. 2024;24(1):110. Published 2024 May 15. doi:10.1186/s12880-024-01261-0 [doi]
4. Ali MB, Gu IY, Berger MS, Jakola AS. A novel federated deep learning scheme for glioma and its subtype classification. Front Neurosci. 2023;17:1181703. Published 2023 May 23. doi:10.3389/fnins.2023.1181703 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/307-02-002