HiPerfGNN uses VQ-VAE on DSC perfusion curves to form hierarchical tumor habitat graphs that predict IDH mutation (AUC 0.96 internal, 0.89 external), 1p/19q codeletion, and WHO grade.
In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition
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Hierarchical Perfusion Graphs for Tumor Heterogeneity Modeling in Glioma Molecular Subtyping
HiPerfGNN uses VQ-VAE on DSC perfusion curves to form hierarchical tumor habitat graphs that predict IDH mutation (AUC 0.96 internal, 0.89 external), 1p/19q codeletion, and WHO grade.