RURANET++: An Unsupervised Learning Method for Diabetic Macular Edema Based on SCSE Attention Mechanisms and Dynamic Multi-Projection Head Clustering

• URL: http://arxiv.org/abs/2502.20224v2
• Date: Fri, 07 Mar 2025 08:17:31 GMT
• Title: RURANET++: An Unsupervised Learning Method for Diabetic Macular Edema Based on SCSE Attention Mechanisms and Dynamic Multi-Projection Head Clustering
• Authors: Wei Yang, Yiran Zhu, Jiayu Shen, Yuhan Tang, Chengchang Pan, Hui He, Yan Su, Honggang Qi,
• Abstract summary: RURANET++ is an unsupervised learning-based automated diagnostic system for Diabetic Macular Edema (DME)<n>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.<n> Experimental results demonstrate superior performance across multiple metrics, achieving maximum accuracy (0.8411), precision (0.8593), recall (0.8411), and F1-score, with exceptional clustering quality.
• Score: 13.423253964156117
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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