Contrastive Domain Disentanglement for Generalizable Medical Image Segmentation. (arXiv:2205.06551v1 [cs.CV])

Efficiently utilizing discriminative features is crucial for convolutional
neural networks to achieve remarkable performance in medical image segmentation
and is also important for model generalization across multiple domains, where
letting model recognize domain-specific and domain-invariant information among
multi-site datasets is a reasonable strategy for domain generalization.
Unfortunately, most of the recent disentangle networks are not directly
adaptable to unseen-domain datasets because of the limitations of offered data
distribution. To tackle this deficiency, we propose Contrastive Domain
Disentangle (CDD) network for …

arxiv cv image medical segmentation