Relevance factor variation autoencoder architecture for analyzing cognitive drawing tests

Abstract

A method for performing predictive operations, the method comprising receiving a classification dataset comprising clock drawing images, generating, using a classifier, one or more classification outputs, the one or more classification outputs comprising one or more identifications of dementia or non-dementia for respective ones of clock drawing images. The classifier comprises one or more weights based on a latent space associated with a relevance factor variational autoencoder (RF-VAE). The RF-VAE comprises an encoder configured to generate the latent space. The RF-VAE comprises a decoder configured to generate reconstructions of the second one or more clock drawings based on the latent space. The latent space comprises one or more latent dimensions representative of one or more unique aspects of variation associated with the second one or more clock drawings. The one or more latent dimensions comprise minimal total correlation between the one or more latent dimensions and two dimensions.