  Fundus image segmentation on unseen domains is challenging, especially for
the over-parameterized deep models trained on the small medical datasets. To
address this challenge, we propose a method named Adaptive Feature-fusion
Neural Network (AFNN) for glaucoma segmentation on unseen domains, which mainly
consists of three modules: domain adaptor, feature-fusion network, and
self-supervised multi-task learning. Specifically, the domain adaptor helps the
pretrained-model fast adapt from other image domains to the medical fundus
image domain. Feature-fusion network and self-supervised multi-task learning
for the encoder and decoder are introduced to improve the domain generalization
ability. In addition, we also design the weighted-dice-loss to improve model
performance on complex optic-cup segmentation tasks. Our proposed method
achieves a competitive performance over existing fundus segmentation methods on
four public glaucoma datasets.
