A large-scale multi-view fundus images dataset and benchmark for diabetic retinopathy grading

Diabetic retinopathy (DR) is the most common complication of diabetes and remains a leading cause of irreversible blindness worldwide. In recent years, many deep learning-based methods have been applied to DR grading, significantly reducing the risk of visual impairment. However, most existing models are trained on datasets composed of single-view fundus images, which cannot provide a comprehensive view of the retina. As a result, these models are constrained by incomplete damage representation and exhibit limited generalization performance. To address this limitation, we constructed the Multi-Field Imaging Dataset for Diabetic Retinopathy Grading (MFIDDR), which includes 34 452 fundus images from 4344 patients. Each image was annotated by seven certified ophthalmologists to ensure the reliability of the labels. To the best of our knowledge, MFIDDR is the first publicly available dataset that simultaneously contains four-view fundus images of the same eye, centered on the macula and optic disc, tangent to the upper and lower horizontal lines of the optic disc, as well as clinical information. Based on this dataset, we conducted extensive benchmarking experiments using various representative methods, establishing a solid baseline for future research. Additionally, we proposed a new multi-view foundation model that integrates information fusion across multi-view data to achieve more accurate DR grading. Experimental results demonstrate that this information fusion-driven strategy significantly improves the performance of DR grading models. The MFIDDR dataset has made available at github.com/mfiddr/MFIDDR .