Benchmarking deep models on retinal fundus disease diagnosis and a large-scale dataset

Retinal fundus imaging contributes to monitoring the vision of patients by providing views of the interior surface of the eyes. Machine learning models greatly aided ophthalmologists in detecting retinal disorders from color fundus images. Hence, the quality of the data is pivotal for enhancing diagnosis algorithms, which ultimately benefits vision care and maintenance. To facilitate further research in this domain, we introduce the Eye Disease Diagnosis and Fundus Synthesis (EDDFS) dataset, comprising 28,877 fundus images. These include 15,000 healthy samples and a diverse range of images depicting various disorders such as diabetic retinopathy, age-related macular degeneration, glaucoma, pathological myopia, hypertension retinopathy, retinal vein occlusion, and Laser photocoagulation. In addition to providing the dataset, we propose a Transformer-joint convolution network for automated eye disease screening. Firstly, a co-attention structure is integrated to capture long-range attention information along with local features. Secondly, a cross-stage feature fusion module is designed to extract multi-level and disease-related information. By leveraging the dataset and our proposed network, we establish benchmarks for disease screening and grading tasks. Our experimental results underscore the network’s proficiency in both multi-label and single-label disease diagnosis, while also showcasing the dataset’s capability in supporting fundus synthesis. (The dataset and code will be available on https://github.com/xia-xx-cv/EDDFS_dataset.)