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

Jola Mirecka, Marjan Famili, Anna Kotanska, Nikolai Juraschko, Beatriz Costa-Gomes, Colin M Palmer, Jeyan Thiyagalingam, Tom Burnley, Mark Basham, Alan R Lowe

Keywords: representation learning, VAE, $\beta$-VAE, affinity, cryo-ET, cryo-electron tommography, structural biology, visual proteomics

Abstract: In this work we present affinity-VAE: a framework for automatic clustering and classification of objects in multidimensional image data based on their similarity. The method expands on the concept of $\beta$-VAEs with an informed similarity-based loss component driven by an affinity matrix. The affinity-VAE is able to create rotationally-invariant, morphologically homogeneous clusters in the latent representation, with improved cluster separation compared with a standard $\beta$-VAE. We explore the extent of latent disentanglement and continuity of the latent spaces on both 2D and 3D image data, including simulated biological electron cryo-tomography (cryo-ET) volumes as an example of a scientific application.

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