Early detection of retinal diseases is one of the most important means of preventing partial or permanent blindness in patients. In this research, a novel multi-label classification system is proposed for the detection of multiple retinal diseases, using fundus images collected from a variety of sources. First, a new multi-label retinal disease dataset, the MuReD dataset, is constructed, using a number of publicly available datasets for fundus disease classification. Next, a sequence of post-processing steps is applied to ensure the quality of the image data and the range of diseases, present in the dataset. For the first time in fundus multi-label disease classification, a transformer-based model optimized through extensive experimentation is used for image analysis and decision making. Numerous experiments are performed to optimize the configuration of the proposed system. It is shown that the approach performs better than state-of-the-art works on the same task by 7.9% and 8.1% in terms of AUC score for disease detection and disease classification, respectively. The obtained results further support the potential applications of transformer-based architectures in the medical imaging field.
翻译:早期发现视网膜疾病是预防患者中部分或全部失明的最重要手段之一。在这一研究中,提议采用新的多标签分类系统,利用从各种来源收集的基金图象,检测多种视网膜疾病。首先,利用一些公开可得的基金型疾病分类数据集,构建了新的多标签视网膜疾病数据集,即MureD数据集。接着,采用一系列后处理步骤,确保数据集中的图像数据质量和疾病范围。在Fundus多标签型疾病分类中,首次使用了通过广泛实验优化的变压器模型进行图像分析和决策。进行了许多实验,以优化拟议系统的配置。这表明,该方法比同一任务上的最新工艺工作表现得更好,分别达到7.9%和8.1%的澳大利亚统一公司疾病检测和疾病分类分数。取得的结果进一步支持了基于变压器的建筑在医学成像领域的潜在应用。