目的:糖尿病视网膜病变(DR)的分类旨在利用图像中的隐含信息进行早期诊断,以防止和减轻病情的进一步恶化。然而,现有的方法通常受到需要在大范围内操作的限制,带注释的数据集显示出显著的优势。此外,数据集中不同类别的样本数量需要均匀分布,因为样本不平衡分布的特征会导致对高频疾病类别的过度关注,而忽略了较不常见但同样重要的疾病类别。因此,迫切需要开发一种能够有效缓解样本分布不平衡问题的新分类方法,从而提高糖尿病视网膜病变分类的准确性。
方法:在这项工作中,我们提议MediDRNet,基于原型对比学习的双分支网络模型。该模型采用原型对比学习,为不同程度的病变创建原型,确保它们代表每个病变级别的核心特征。它通过比较数据点及其类别原型之间的相似性来进行分类。我们的双分支网络结构通过强调视网膜病变的细微差异,有效解决了类别不平衡的问题,并提高了分类准确性。此外,我们的方法将双分支网络与特定病变级原型相结合,用于核心特征表示,并结合卷积块注意模块,用于增强病变特征识别.
结果:我们使用Kaggle和UWF分类数据集进行的实验表明,与业内其他高级模型相比,MediDRNet表现出卓越的性能。特别是在UWFDR分类数据集上,它在所有指标上都实现了最先进的性能。在KaggleDR分类数据集上,它达到了最高的平均分类精度(0.6327)和Macro-F1得分(0.6361)。特别是在Kaggle数据集(1、2、3和4级)上的少数类别糖尿病视网膜病变的分类任务中,该模型达到了58.08%的高分类精度,55.32%,69.73%,和90.21%,分别。在消融研究中,与其他特征提取方法相比,MediDRNet模型在糖尿病视网膜眼底图像的特征提取中被证明更有效。
结论:本研究采用原型对比学习和双向分支学习策略,在不平衡的糖尿病性视网膜病变数据集中成功构建了糖尿病性视网膜病变分级系统。通过双分支网络,特征学习分支有效地促进了特征从分级网络到分类学习分支的平稳过渡,准确识别少数民族样本类别。该方法不仅有效解决了样本失衡的问题,而且为临床应用中糖尿病视网膜病变的精确分级和早期诊断提供了有力支持。展示了在处理复杂的糖尿病视网膜病变数据集时的卓越性能。此外,这项研究显著提高了医疗实践中糖尿病视网膜病变患者疾病进展的预防和管理效率.我们鼓励使用和修改我们的代码,可在GitHub上公开访问:https://github.com/ReinforceLove/MediDRNet。
OBJECTIVE: The classification of diabetic retinopathy (DR) aims to utilize the implicit information in images for early diagnosis, to prevent and mitigate the further worsening of the condition. However, existing methods are often limited by the need to operate within large, annotated datasets to show significant advantages. Additionally, the number of samples for different categories within the dataset needs to be evenly distributed, because the characteristic of sample imbalance distribution can lead to an excessive focus on high-frequency disease categories, while neglecting the less common but equally important disease categories. Therefore, there is an urgent need to develop a new classification method that can effectively alleviate the issue of sample distribution imbalance, thereby enhancing the accuracy of diabetic retinopathy classification.
METHODS: In this work, we propose MediDRNet, a dual-branch network model based on prototypical contrastive learning. This model adopts prototype contrastive learning, creating prototypes for different levels of lesions, ensuring they represent the core features of each lesion level. It classifies by comparing the similarity between data points and their category prototypes. Our dual-branch network structure effectively resolves the issue of category imbalance and improves classification accuracy by emphasizing subtle differences in retinal lesions. Moreover, our approach combines a dual-branch network with specific lesion-level prototypes for core feature representation and incorporates the convolutional block attention module for enhanced lesion feature identification.
RESULTS: Our experiments using both the Kaggle and UWF classification datasets have demonstrated that MediDRNet exhibits exceptional performance compared to other advanced models in the industry, especially on the UWF DR classification dataset where it achieved state-of-the-art performance across all metrics. On the Kaggle DR classification dataset, it achieved the highest average classification accuracy (0.6327) and Macro-F1 score (0.6361). Particularly in the classification tasks for minority categories of diabetic retinopathy on the Kaggle dataset (Grades 1, 2, 3, and 4), the model reached high classification accuracies of 58.08%, 55.32%, 69.73%, and 90.21%, respectively. In the ablation study, the MediDRNet model proved to be more effective in feature extraction from diabetic retinal fundus images compared to other feature extraction methods.
CONCLUSIONS: This study employed prototype contrastive learning and bidirectional branch learning strategies, successfully constructing a grading system for diabetic retinopathy lesions within imbalanced diabetic retinopathy datasets. Through a dual-branch network, the feature learning branch effectively facilitated a smooth transition of features from the grading network to the classification learning branch, accurately identifying minority sample categories. This method not only effectively resolved the issue of sample imbalance but also provided strong support for the precise grading and early diagnosis of diabetic retinopathy in clinical applications, showcasing exceptional performance in handling complex diabetic retinopathy datasets. Moreover, this research significantly improved the efficiency of prevention and management of disease progression in diabetic retinopathy patients within medical practice. We encourage the use and modification of our code, which is publicly accessible on GitHub: https://github.com/ReinforceLove/MediDRNet.