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In this work we present affinity-VAE: a framework for automatic clusteringand classification of objects in multidimensional image data based on theirsimilarity. The method expands on the concept of $\beta$-VAEs with an informedsimilarity-based loss component driven by an affinity matrix. The affinity-VAEis able to create rotationally-invariant, morphologically homogeneous clustersin the latent representation, with improved cluster separation compared with astandard $\beta$-VAE. We explore the extent of latent disentanglement andcontinuity of the latent spaces on both 2D and 3D image data, includingsimulated biological electron cryo-tomography (cryo-ET) volumes as an exampleof a scientific application.

Affinity-VAE for disentanglement, clustering and classification of objects in multidimensional image data