Diabetic Macular Edema (DME), a prevalent complication among diabetic patients, constitutes a major cause of visual impairment and blindness. Although deep learning has achieved remarkable progress in medical image analysis, traditional DME diagnosis still relies on extensive annotated data and subjective ophthalmologist assessments, limiting practical applications. To address this, we present RURANET++, an unsupervised learning-based automated DME diagnostic system. This framework incorporates an optimized U-Net architecture with embedded Spatial and Channel Squeeze & Excitation (SCSE) attention mechanisms to enhance lesion feature extraction. During feature processing, a pre-trained GoogLeNet model extracts deep features from retinal images, followed by PCA-based dimensionality reduction to 50 dimensions for computational efficiency. Notably, we introduce a novel clustering algorithm employing multi-projection heads to explicitly control cluster diversity while dynamically adjusting similarity thresholds, thereby optimizing intra-class consistency and inter-class discrimination. Experimental results demonstrate superior performance across multiple metrics, achieving maximum accuracy (0.8411), precision (0.8593), recall (0.8411), and F1-score (0.8390), with exceptional clustering quality. This work provides an efficient unsupervised solution for DME diagnosis with significant clinical implications.

翻译：糖尿病性黄斑水肿（DME）是糖尿病患者中一种普遍并发症，是导致视力损伤和失明的主要原因。尽管深度学习在医学图像分析领域已取得显著进展，但传统的DME诊断仍依赖于大量标注数据和眼科医师的主观评估，限制了其实际应用。为此，我们提出了RURANET++，一种基于无监督学习的自动化DME诊断系统。该框架采用优化的U-Net架构，并嵌入了空间与通道挤压激励（SCSE）注意力机制，以增强病灶特征提取能力。在特征处理阶段，使用预训练的GoogLeNet模型从视网膜图像中提取深度特征，随后通过基于PCA的方法将特征降维至50维以提高计算效率。值得注意的是，我们引入了一种新颖的聚类算法，该算法采用多投影头来显式控制簇的多样性，同时动态调整相似度阈值，从而优化类内一致性与类间区分度。实验结果表明，该方法在多项指标上均表现出优越性能，取得了最高的准确率（0.8411）、精确率（0.8593）、召回率（0.8411）和F1分数（0.8390），并展现出卓越的聚类质量。本研究为DME诊断提供了一种高效的无监督解决方案，具有重要的临床意义。