Adaptive Feature Fusion Neural Network for Glaucoma Segmentation on Unseen Fundus Images
<|reference_start|>Adaptive Feature Fusion Neural Network for Glaucoma Segmentation on Unseen Fundus Images: 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.<|reference_end|>
@article{zhong2024adaptive,
    title={Adaptive Feature Fusion Neural Network for Glaucoma Segmentation on
  Unseen Fundus Images},
    author={Jiyuan Zhong and Hu Ke and Ming Yan},
    journal={arXiv preprint arXiv:2404.02084},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.02084},
    primaryClass={cs.CV}
}