2021
DOI: 10.48550/arxiv.2106.08188
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Optimal Latent Vector Alignment for Unsupervised Domain Adaptation in Medical Image Segmentation

Abstract: This paper addresses the domain shift problem for segmentation. As a solution, we propose OLVA, a novel and lightweight unsupervised domain adaptation method based on a Variational Auto-Encoder (VAE) and optimal transport (OT) theory. Thanks to the VAE, our model learns a shared cross-domain latent space that follows a normal distribution, which reduces the domain shift. To guarantee valid segmentations, our shared latent space is designed to model the shape rather than the intensity variations. We further rel… Show more

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