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Modeling Barrett's Esophagus Progression using Geometric Variational Autoencoders

arXiv 2303.12711 ↗First posted 17 Mar 2023eess.IV cs.CV cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 19 references that are in the map. First posted on arXiv 17 Mar 2023.

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11 of 19 references.

References · 11

  1. 2016Group Equivariant Convolutional Networkscited by 1,165
  2. 2018Hyperspherical Variational Auto-Encoderscited by 189
  3. 2017Latent Space Oddity: on the Curvature of Deep Generative Modelscited by 174
  4. 2019Constant Curvature Graph Convolutional Networkscited by 108
  5. 2018Roto-Translation Covariant Convolutional Networks for Medical Image Analysiscited by 92
  6. 2019Mixed-curvature Variational Autoencoderscited by 86
  7. 2017The Riemannian Geometry of Deep Generative Modelscited by 82
  8. 2017Metrics for Deep Generative Modelscited by 53
  9. 2014Metrics for Probabilistic Geometriescited by 28
  10. 2020Geometry-Aware Hamiltonian Variational Auto-Encodercited by 10
  11. 2020Orientation-Disentangled Unsupervised Representation Learning for Computational Pathologycited by 2

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