Adaptive Feature Fusion Neural Network for Glaucoma Segmentation on Unseen Fundus Images

• URL: http://arxiv.org/abs/2404.02084v1
• Date: Tue, 2 Apr 2024 16:30:12 GMT
• Title: Adaptive Feature Fusion Neural Network for Glaucoma Segmentation on Unseen Fundus Images
• Authors: Jiyuan Zhong, Hu Ke, Ming Yan,
• Abstract summary: We propose a method named Adaptive Feature-fusion Neural Network (AFNN) for glaucoma segmentation on unseen domains. The domain adaptor helps the pretrained-model fast adapt from other image domains to the medical fundus image domain. Our proposed method achieves a competitive performance over existing fundus segmentation methods on four public glaucoma datasets.
• Score: 13.03504366061946
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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