Modeling Barrett's Esophagus Progression using Geometric Variational Autoencoders
Image and Video Processing
2025-06-17 v3 Computer Vision and Pattern Recognition
Machine Learning
Abstract
Early detection of Barrett's Esophagus (BE), the only known precursor to Esophageal adenocarcinoma (EAC), is crucial for effectively preventing and treating esophageal cancer. In this work, we investigate the potential of geometric Variational Autoencoders (VAEs) to learn a meaningful latent representation that captures the progression of BE. We show that hyperspherical VAE (S-VAE) and Kendall Shape VAE show improved classification accuracy, reconstruction loss, and generative capacity. Additionally, we present a novel autoencoder architecture that can generate qualitative images without the need for a variational framework while retaining the benefits of an autoencoder, such as improved stability and reconstruction quality.
Keywords
Cite
@article{arxiv.2303.12711,
title = {Modeling Barrett's Esophagus Progression using Geometric Variational Autoencoders},
author = {Vivien van Veldhuizen and Sharvaree Vadgama and Onno J. de Boer and Sybren Meijer and Erik J. Bekkers},
journal= {arXiv preprint arXiv:2303.12711},
year = {2025}
}