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Found 4 papers, 4 papers with code
Test-Time Adaptation with Shape Moments for Image Segmentation
In typical clinical settings, the source data is inaccessible and the target distribution is represented with a handful of samples: adaptation can only happen at test time on a few or even a single subject(s).
Source-Free Domain Adaptation for Image Segmentation
Our method yields comparable results to several state of the art adaptation techniques, despite having access to much less information, as the source images are entirely absent in our adaptation phase.
Source-Relaxed Domain Adaptation for Image Segmentation
Our formulation is based on minimizing a label-free entropy loss defined over target-domain data, which we further guide with a domain invariant prior on the segmentation regions.
Constrained domain adaptation for Image segmentation
We propose to adapt segmentation networks with a constrained formulation, which embeds domain-invariant prior knowledge about the segmentation regions.
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