一种用于弱监督白内障眼底图像增强的两阶段多尺度注意力网络
A Two-Stage Multi-Scale Attention-Based Network for Weakly Supervised Cataract Fundus Image Enhancement
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中文总结 AI 辅助
本文提出两阶段多尺度注意力网络TSMSA-Net，通过域变换合成配对图像，实现无配对数据的弱监督白内障眼底图像增强，并在Kaggle和ODIR-5K上超越现有方法，提升血管分割与分类性能。
中文摘要 AI 辅助
白内障是导致视力丧失的主要原因，并阻碍进一步的诊断。然而，白内障眼底图像增强常常面临诸如配对的白内障视网膜图像有限以及视网膜图像中细节恢复不足等挑战。为缓解这些挑战，本文提出了一种用于弱监督白内障眼底图像增强的两阶段多尺度注意力网络（TSMSA-Net）。我们的TSMSA-Net利用域变换来合成配对的真实感白内障图像，解决了配对图像难以获取的问题。为了进一步从眼底图像中提取细节信息并减少增强过程中伪影的产生，我们提出了一个基于多尺度注意力的阶段，以学习更有用的特征用于白内障图像增强。在Kaggle和ODIR-5K上的实验结果表明，即使在没有配对图像的情况下，我们的TSMSA-Net也优于当前最先进的白内障眼底图像增强方法，并展现出一定的泛化能力。此外，该增强还能提高白内障图像中血管分割和分类的性能。
英文摘要
Cataract is a major cause of vision loss and hinders further diagnosis. However, cataract fundus image enhancement often grapples with challenges such as limited paired cataract retinal images and insufficient recovery of fine details in the retinal images. To mitigate these challenges, we in this paper propose a two-stage multi-scale attention-based network (TSMSA-Net) for weakly supervised cataract fundus image enhancement. Our TSMSA-Net leverages the domain transformation to synthesis paired real-like cataract images, solving the problem of difficult acquisition of paired images. To further extract detailed information from fundus images and reduce the generation of artifacts during the enhancement process, we propose a multi-scale attention-based stage to learn more useful features for cataract image enhancement. Experimental results on Kaggle and ODIR-5K demonstrate that our TSMSA-Net outperforms current state-of-the-art cataract fundus images enhancement even without paired images and exhibits certain generalization ability. Experimental results on Kaggle and ODIR-5K datasets indicate that our TSMSA-Net outperforms the current state-of-the-art methods for cataract fundus image enhancement, even in the absence of paired images. Additionally, it demonstrates a certain level of generalization capability. The enhancement also can improve the performance of vessel segmentation and classification in cataract images.
发表机构
- Hunan Institute of Technology（湖南工学院）
- National and Local Joint Engineering Laboratory of Computer Aided Design（计算机辅助设计国家地方联合工程实验室）
- Dalian University of Technology（大连理工大学）
机构由 AI 辅助整理，请以论文原文为准。
