A simple convolutional autoencoder on ADNI brain scans learns latent spaces linked to Alzheimer's progression; the new LRCP framework plus SHAP analysis identifies which atlas regions carry the clinically relevant information.
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K-fold CUBV combines cross-validation with PAC-Bayesian upper bounds on actual risk to provide a more robust criterion for validating ML accuracy and reducing false positives than standard CV.
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Latent space projections and atlases: A cautionary tale in deep neuroimaging using autoencoders
A simple convolutional autoencoder on ADNI brain scans learns latent spaces linked to Alzheimer's progression; the new LRCP framework plus SHAP analysis identifies which atlas regions carry the clinically relevant information.
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Is K-fold cross validation the best model selection method for Machine Learning?
K-fold CUBV combines cross-validation with PAC-Bayesian upper bounds on actual risk to provide a more robust criterion for validating ML accuracy and reducing false positives than standard CV.