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.

Background: Diabetic Retinopathy (DR) is a major cause of blindness worldwide.Early detection and treatment are crucial to prevent vision loss, making accurateand timely diagnosis critical. Deep learning technology has shown promise in theautomated diagnosis of DR, and in particular, multi-lesion segmentation tasks. Inthis paper, we propose a novel Transformer-based model for DR segmentationthat incorporates hyperbolic embeddings and a spatial prior module. Methods: Our model is primarily built on a traditional Vision Transformerencoder and further enhanced by incorporating a spatial prior module for imageconvolution and feature continuity, followed by feature interaction processingusing the spatial feature injector and extractor. Hyperbolic embeddings are usedto classify feature matrices from the model at the pixel level. We evaluated theproposed model’s performance on the publicly available datasets and compared itwith other widely used DR segmentation models. The results show that ourmodel outperforms these widely used DR segmentation models. Results: The incorporation of hyperbolic embeddings and a spatial prior moduleinto the Vision Transformer-based model significantly improves the accuracy ofDR segmentation. The hyperbolic embeddings enable us to better capture theunderlying geometric structure of the feature matrices, which is important foraccurate segmentation. The spatial prior module improves the continuity of thefeatures and helps to better distinguish between lesions and normal tissues.Overall, our proposed model has potential for clinical use in automated DRdiagnosis, improving accuracy and speed of diagnosis. Conclusions: Our study shows that the integration of hyperbolic embeddingsand a spatial prior module with a Vision Transformer-based model improves theperformance of DR segmentation models. Future research can explore theapplication of our model to other medical imaging tasks, as well as furtheroptimization and validation in real-world clinical settings.