Adaptive Feature Fusion Neural Network for Glaucoma Segmentation on Unseen Fundus Images
This addresses domain generalization for glaucoma segmentation in medical imaging, but it appears incremental as it builds on existing methods with specific modules.
The paper tackles the challenge of glaucoma segmentation on unseen fundus image domains by proposing an Adaptive Feature-fusion Neural Network (AFNN), which achieves competitive performance on four public datasets.
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.