تقرير
Adaptive Feature Fusion Neural Network for Glaucoma Segmentation on Unseen Fundus Images
| العنوان: | Adaptive Feature Fusion Neural Network for Glaucoma Segmentation on Unseen Fundus Images |
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| المؤلفون: | Zhong, Jiyuan, Ke, Hu, Yan, Ming |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | 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. Comment: 17 pages, 11 figures |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2404.02084 |
| رقم الأكسشن: | edsarx.2404.02084 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |