面向未见眼底图像青光眼分割的自适应特征融合神经网络
Adaptive Feature Fusion Neural Network for Glaucoma Segmentation on Unseen Fundus Images
AI总结：
本文提出自适应特征融合神经网络AFNN，通过域适配器、特征融合网络和自监督多任务学习提升青光眼眼底分割的域泛化能力，并在四个公开数据集上取得竞争性结果。
摘要：
在未见域上的眼底图像分割具有挑战性，尤其是对于在小型医学数据集上训练的过参数化深度模型而言。为应对这一挑战，我们提出了一种名为自适应特征融合神经网络（AFNN）的方法，用于未见域上的青光眼分割，该方法主要由三个模块组成：域适配器、特征融合网络和自监督多任务学习。具体而言，域适配器帮助预训练模型从其他图像域快速适配到医学眼底图像域。引入特征融合网络以及针对编码器和解码器的自监督多任务学习，以提高域泛化能力。此外，我们还设计了加权 Dice 损失，以提升模型在复杂视杯分割任务上的性能。我们提出的方法在四个公开青光眼数据集上取得了优于现有眼底分割方法的竞争性表现。
英文摘要：
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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