MAP: Domain Generalization via Meta-Learning on Anatomy-Consistent
  Pseudo-Modalities
Dewei Hu Hao Li Han Liu Xing Yao Jiacheng Wang Ipek Oguz
Deep models suffer from limited generalization capability to unseen domains,
which has severely hindered their clinical applicability. Specifically for the
retinal vessel segmentation task, although the model is supposed to learn the
anatomy of the target, it can be distracted by confounding factors like
intensity and contrast. We propose Meta learning on Anatomy-consistent
Pseudo-modalities (MAP), a method that improves model generalizability by
learning structural features. We first leverage a feature extraction network to
generate three distinct pseudo-modalities that share the vessel structure of
the original image. Next, we use the episodic learning paradigm by selecting
one of the pseudo-modalities as the meta-train dataset, and perform
meta-testing on a continuous augmented image space generated through Dirichlet
mixup of the remaining pseudo-modalities. Further, we introduce two loss
functions that facilitate the model's focus on shape information by clustering
the latent vectors obtained from images featuring identical vasculature. We
evaluate our model on seven public datasets of various retinal imaging
modalities and we conclude that MAP has substantially better generalizability.
Our code is publically available at https://github.com/DeweiHu/MAP.