Diabetic Retinopathy (DR) is a major cause of blindness worldwide. Early detection and treatment are crucial to prevent vision loss, making accurate and timely diagnosis critical. Deep learning technology has shown promise in the automated diagnosis of DR, and in particular, multi-lesion segmentation tasks. In this paper, we propose a novel Transformer-based model for DR segmentation that incorporates hyperbolic embeddings and a spatial prior module. The proposed model is primarily built on a traditional Vision Transformer encoder and further enhanced by incorporating a spatial prior module for image convolution and feature continuity, followed by feature interaction processing using the spatial feature injector and extractor. Hyperbolic embeddings are used to classify feature matrices from the model at the pixel level. We evaluated the proposed model’s performance on the publicly available datasets and compared it with other widely used DR segmentation models. The results show that our model outperforms these widely used DR segmentation models. The incorporation of hyperbolic embeddings and a spatial prior module into the Vision Transformer-based model significantly improves the accuracy of DR segmentation. The hyperbolic embeddings enable us to better capture the underlying geometric structure of the feature matrices, which is important for accurate segmentation. The spatial prior module improves the continuity of the features and helps to better distinguish between lesions and normal tissues. Overall, our proposed model has potential for clinical use in automated DR diagnosis, improving accuracy and speed of diagnosis. Our study shows that the integration of hyperbolic embeddings and a spatial prior module with a Vision Transformer-based model improves the performance of DR segmentation models. Future research can explore the application of our model to other medical imaging tasks, as well as further optimization and validation in real-world clinical settings.

糖尿病视网膜病变（DR）是全世界失明的主要原因。早期发现和治疗对于预防视力丧失至关重要，因此准确、及时的诊断至关重要。深度学习技术在 DR 的自动诊断，特别是多病灶分割任务中显示出了前景。在本文中，我们提出了一种基于 Transformer 的新型 DR 分割模型，该模型结合了双曲嵌入和空间先验模块。所提出的模型主要建立在传统的 Vision Transformer 编码器的基础上，并通过合并用于图像卷积和特征连续性的空间先验模块进一步增强，然后使用空间特征注入器和提取器进行特征交互处理。双曲嵌入用于在像素级别对模型中的特征矩阵进行分类。我们评估了所提出的模型在公开数据集上的性能，并将其与其他广泛使用的 DR 分割模型进行了比较。结果表明，我们的模型优于这些广泛使用的 DR 分割模型。将双曲嵌入和空间先验模块纳入基于 Vision Transformer 的模型中，显着提高了 DR 分割的准确性。双曲嵌入使我们能够更好地捕获特征矩阵的基础几何结构，这对于准确分割非常重要。空间先验模块提高了特征的连续性，有助于更好地区分病变和正常组织。总体而言，我们提出的模型具有在自动化 DR 诊断中临床应用的潜力，可提高诊断的准确性和速度。我们的研究表明，双曲嵌入和空间先验模块与基于 Vision Transformer 的模型的集成提高了 DR 分割模型的性能。未来的研究可以探索我们的模型在其他医学成像任务中的应用，以及在现实临床环境中的进一步优化和验证。