Abstract
Barrett's Esophagus (BE) is the only precursor known to Esophageal Adenocarcinoma (EAC), a type of esophageal cancer with poor prognosis upon diagnosis. Therefore, diagnosing BE is crucial in preventing and treating esophageal cancer. While supervised machine learning supports BE diagnosis, high interobserver variability in histopathological training data limits these methods. Unsupervised representation learning via Variational Autoencoders (VAEs) shows promise, as they map input data to a lower-dimensional manifold with only useful features, characterizing BE progression for improved downstream tasks and insights. However, the VAE's Euclidean latent space distorts point relationships, hindering disease progression modeling. Geometric VAEs provide additional geometric structure to the latent space, with RHVAE assuming a Riemannian manifold and $\mathcal{S}$-VAE a hyperspherical manifold. Our study shows that $\mathcal{S}$-VAE outperforms vanilla VAE with better reconstruction losses, representation classification accuracies, and higher-quality generated images and interpolations in lower-dimensional settings. By disentangling rotation information from the latent space, we improve results further using a group-based architecture. Additionally, we take initial steps towards $\mathcal{S}$-AE, a novel autoencoder model generating qualitative images without a variational framework, but retaining benefits of autoencoders such as stability and reconstruction quality.
Abstract (translated)
Barrett's esophagus (BE) 是已知柠檬汁癌(EAC)的已知前驱体,而这种癌症在诊断时具有较差的预后。因此,诊断BE在预防和治愈柠檬汁癌方面至关重要。虽然监督机器学习支持BE诊断,但病理学训练数据的高互现性限制了这些方法。无监督的变分自编码器(VAE)的表现令人鼓舞,因为它们将输入数据映射到一个只有有用特征的低维多态,以 characterized BE 的进展,并提高后续任务和洞察力。然而,VAE 的欧几里得 latent space 扭曲了点关系,妨碍了疾病进展建模。几何VAE为 latent space 提供了额外的几何结构,其中 RHVAE 假设一个黎曼多态,而 $\mathcal{S}$-VAE 是一个高斯多态。我们的研究表明,$\mathcal{S}$-VAE 在低维环境中比传统VAE有更好的重构损失、表示分类精度和更高质量的生成图像和插值。通过从 latent space 中分离旋转信息,我们使用群体架构进一步改进结果。此外,我们开始朝着 $\mathcal{S}$-AE 迈进,这是一种没有变分框架的新型自编码器模型,生成高质量的定性图像,但保留了自编码器的稳定性和重构质量。
URL
https://arxiv.org/abs/2303.12711
