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

2022-09-09 20:39:22
Jola Mirecka, Marjan Famili, Anna Kotańska, Nikolai Juraschko, Beatriz Costa-Gomes, Colin M. Palmer, Jeyan Thiyagalingam, Tom Burnley, Mark Basham, Alan R. Lowe
arXiv_CV
     
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.

Abstract (translated)

URL

https://arxiv.org/abs/2209.04517

PDF

https://arxiv.org/pdf/2209.04517.pdf

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