Multi-view VAE fuses T1Gd and FLAIR radiomics to predict MGMT methylation status in GBM with test AUC 0.77, outperforming baseline (0.54) and tuned (0.64) radiomics models.
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A variational quantum autoencoder detects anomalies in brain MRI by scoring resistance to compression, reporting slice-level ROC-AUC of 0.95 and outperforming classical autoencoders and PCA on public datasets.
POTTERS extends the Potts model with generalized spatial dependence and external priors for Bayesian remote sensing image segmentation via variational inference, without needing target-region labels.
Reviews linear and nonlinear SciML surrogates for coupled fluid flow and transport, with new PINN modeling of turbidity currents and β-VAE mode extraction from Rayleigh-Bénard convection.
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The Multi-View Paradigm Shift in MRI Radiomics: Predicting MGMT Methylation in Glioblastoma
Multi-view VAE fuses T1Gd and FLAIR radiomics to predict MGMT methylation status in GBM with test AUC 0.77, outperforming baseline (0.54) and tuned (0.64) radiomics models.
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Compression-Driven Anomaly Detection in Brain MRI Using an Interpretable Quantum Autoencoder
A variational quantum autoencoder detects anomalies in brain MRI by scoring resistance to compression, reporting slice-level ROC-AUC of 0.95 and outperforming classical autoencoders and PCA on public datasets.
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Scalable Bayesian Spatial Mixture Modelling for Remote Sensing Image Segmentation
POTTERS extends the Potts model with generalized spatial dependence and external priors for Bayesian remote sensing image segmentation via variational inference, without needing target-region labels.
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Advances in Scientific Machine Learning for Coupled Fluid Flow and Transport
Reviews linear and nonlinear SciML surrogates for coupled fluid flow and transport, with new PINN modeling of turbidity currents and β-VAE mode extraction from Rayleigh-Bénard convection.