A supervised subtype differentiation learning for building invariant features of non-small cell lung cancer in a latent space of a Variational Autoencoder

Fabian Cano; Charlens Alvarez-Jimenez; David Becerra; Andres Siabatto; Angel Cruz-Roa; Eduardo Romero

doi:10.1117/12.2606255

10 December 2021 A supervised subtype differentiation learning for building invariant features of non-small cell lung cancer in a latent space of a Variational Autoencoder

Fabian Cano, Charlens Alvarez-Jimenez, David Becerra, Andres Siabatto, Angel Cruz-Roa, Eduardo Romero

Author Affiliations +

Proceedings Volume 12088, 17th International Symposium on Medical Information Processing and Analysis; 120880Y (2021) https://doi.org/10.1117/12.2606255
Event: Seventeenth International Symposium on Medical Information Processing and Analysis, 2021, Campinas, Brazil

Abstract

This work presents a novel quantification of the cancer extension using a latent space embedded metrics of a variational autoencoder which captures the invariant patterns of the disease and projects them into a smaller latent space where data relations are linear, making it possible to apply simple metrics to quantify complicated relations. Selected patches of non-small cell lung cancer are projected to such latent space and a logistic regression model assigns an Euclidean distance between the patches projected in space. A simple grouping strategy quantitatively stratifies the characteristic patterns of the most representative patches for both adenocarcinoma and squamous cell lung cancer classes but it also estimates the composition of a mixture of patterns. This approach is fully interpretable, integrable with a pathology work flow and an objective characterization of diseases with complex patterns.

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Fabian Cano, Charlens Alvarez-Jimenez, David Becerra, Andres Siabatto, Angel Cruz-Roa, and Eduardo Romero "A supervised subtype differentiation learning for building invariant features of non-small cell lung cancer in a latent space of a Variational Autoencoder", Proc. SPIE 12088, 17th International Symposium on Medical Information Processing and Analysis, 120880Y (10 December 2021); https://doi.org/10.1117/12.2606255

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