People with diabetes are more likely to develop diabetic retinopathy (DR) than healthy people. However, DR is the leading cause of blindness. At present, the diagnosis of diabetic retinopathy mainly relies on the experienced clinician to recognize the fine features in color fundus images. This is a time-consuming task. Therefore, in this paper, to promote the development of UW-OCTA DR automatic detection, we propose a novel semi-supervised semantic segmentation method for UW-OCTA DR image grade assessment. This method, first, uses the MAE algorithm to perform semi-supervised pre-training on the UW-OCTA DR grade assessment dataset to mine the supervised information in the UW-OCTA images, thereby alleviating the need for labeled data. Secondly, to more fully mine the lesion features of each region in the UW-OCTA image, this paper constructs a cross-algorithm ensemble DR tissue segmentation algorithm by deploying three algorithms with different visual feature processing strategies. The algorithm contains three sub-algorithms, namely pre-trained MAE, ConvNeXt, and SegFormer. Based on the initials of these three sub-algorithms, the algorithm can be named MCS-DRNet. Finally, we use the MCS-DRNet algorithm as an inspector to check and revise the results of the preliminary evaluation of the DR grade evaluation algorithm. The experimental results show that the mean dice similarity coefficient of MCS-DRNet v1 and v2 are 0.5161 and 0.5544, respectively. The quadratic weighted kappa of the DR grading evaluation is 0.7559. Our code will be released soon.

翻译：糖尿病人比健康人更有可能发展糖尿病 retinod(DR) 。 但是, DR 是导致失明的主要原因。 目前,糖尿病 retinopat病的诊断主要依靠有经验的临床医生来识别彩色基金图像中的精细特征。 这是一个耗时的任务。 因此,在本文中,为了促进UW-OCTA DR自动检测的发展,我们提议为 UW-OCTA DR 图像等级评估采用新型半监督的语义分解法。 这种方法首先使用MAE 算法对 UW-OCTA DR 图像中的受监督信息进行半监督的预培训。 从而减轻了对贴标签数据的需求。 其次,为了更全面地清除UW-OCTA DR 图像中每个区域的腐蚀性特征, 本文将构建一种跨等离子的 DRET 结构分解算法, 采用三种具有不同视觉特征处理策略的算法。 算法包含三个对UW- OCTADR2 的亚值 和MADR 的亚值 。