Yu Chenglin, Pei Hailong
Key Laboratory of Autonomous Systems and Networked Control, Ministry of Education, Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong, South China University of Technology, Guangzhou 510640, China.
Key Laboratory of Autonomous Systems and Networked Control, Ministry of Education, Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong, School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China.
Diagnostics (Basel). 2023 Oct 19;13(20):3251. doi: 10.3390/diagnostics13203251.
Diabetic retinopathy (DR) is a common complication of diabetes, which can lead to vision loss. Early diagnosis is crucial to prevent the progression of DR. In recent years, deep learning approaches have shown promising results in the development of an intelligent and efficient system for DR classification. However, one major drawback is the need for expert-annotated datasets, which are both time-consuming and costly. To address these challenges, this paper proposes a novel dynamic graph clustering learning (DGCL) method for unsupervised classification of DR, which innovatively deploys the Euclidean and topological features from fundus images for dynamic clustering. Firstly, a multi-structural feature fusion (MFF) module extracts features from the structure of the fundus image and captures topological relationships among multiple samples, generating a fused representation. Secondly, another consistency smoothing clustering (CSC) module combines network updates and deep clustering to ensure stability and smooth performance improvement during model convergence, optimizing the clustering process by iteratively updating the network and refining the clustering results. Lastly, dynamic memory storage is utilized to track and store important information from previous iterations, enhancing the training stability and convergence. During validation, the experimental results with public datasets demonstrated the superiority of our proposed DGCL network.
糖尿病视网膜病变（DR）是糖尿病的一种常见并发症，可导致视力丧失。早期诊断对于预防DR的进展至关重要。近年来，深度学习方法在开发用于DR分类的智能高效系统方面显示出了有前景的结果。然而，一个主要缺点是需要专家标注的数据集，这既耗时又昂贵。为应对这些挑战，本文提出了一种用于DR无监督分类的新型动态图聚类学习（DGCL）方法，该方法创新性地利用眼底图像的欧几里得和拓扑特征进行动态聚类。首先，一个多结构特征融合（MFF）模块从眼底图像结构中提取特征并捕捉多个样本之间的拓扑关系，生成融合表示。其次，另一个一致性平滑聚类（CSC）模块结合网络更新和深度聚类，以确保模型收敛期间的稳定性和性能平稳提升，通过迭代更新网络和优化聚类结果来优化聚类过程。最后，利用动态内存存储来跟踪和存储来自先前迭代的重要信息，增强训练稳定性和收敛性。在验证期间，使用公共数据集的实验结果证明了我们提出的DGCL网络的优越性。
