1 code implementation • 16 May 2022 • Mathilde Bateson, Hervé Lombaert, Ismail Ben Ayed
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).
1 code implementation • 6 Aug 2021 • Mathilde Bateson, Hoel Kervadec, Jose Dolz, Hervé Lombaert, Ismail Ben Ayed
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.
5 code implementations • 7 May 2020 • Mathilde Bateson, Hoel Kervadec, Jose Dolz, Herve Lombaert, Ismail Ben Ayed
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.
1 code implementation • 8 Aug 2019 • Mathilde Bateson, Jose Dolz, Hoel Kervadec, Hervé Lombaert, Ismail Ben Ayed
We propose to adapt segmentation networks with a constrained formulation, which embeds domain-invariant prior knowledge about the segmentation regions.