Modeling Barrett's Esophagus Progression using Geometric Variational Autoencoders
This work addresses early detection of esophageal cancer precursors, but it appears incremental as it builds on existing VAE methods with specific architectural modifications.
The researchers tackled the problem of early detection of Barrett's Esophagus progression by using geometric Variational Autoencoders to learn latent representations, resulting in improved classification accuracy, reconstruction loss, and generative capacity.
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.