Adaptive Feature Fusion Neural Network for Glaucoma Segmentation on Unseen Fundus Images
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911824580968448 |
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| author | Zhong, Jiyuan Ke, Hu Yan, Ming |
| author_facet | Zhong, Jiyuan Ke, Hu Yan, Ming |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_02084 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Adaptive Feature Fusion Neural Network for Glaucoma Segmentation on Unseen Fundus Images Zhong, Jiyuan Ke, Hu Yan, Ming Computer Vision and Pattern Recognition 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. |
| title | Adaptive Feature Fusion Neural Network for Glaucoma Segmentation on Unseen Fundus Images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.02084 |