Semi-supervised Meta-learning with Disentanglement for Domain-generalised Medical Image Segmentation

@inproceedings{Liu2021SemisupervisedMW,
  title={Semi-supervised Meta-learning with Disentanglement for Domain-generalised Medical Image Segmentation},
  author={Xiao Liu and Spyridon Thermos and Alison Q. O'Neil and Sotirios A. Tsaftaris},
  booktitle={MICCAI},
  year={2021}
}
Generalising deep models to new data from new centres (termed here domains) remains a challenge. This is largely attributed to shifts in data statistics (domain shifts) between source and unseen domains. Recently, gradient-based meta-learning approaches where the training data are split into meta-train and meta-test sets to simulate and handle the domain shifts during training have shown improved generalisation performance. However, the current fully supervised meta-learning approaches are not… Expand

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