DRStageNet: Deep Learning for Diabetic Retinopathy Staging from Fundus
  Images
Yevgeniy Men Jonathan Fhima Leo Anthony Celi Lucas Zago Ribeiro Luis Filipe Nakayama Joachim A. Behar
Diabetic retinopathy (DR) is a prevalent complication of diabetes associated
with a significant risk of vision loss. Timely identification is critical to
curb vision impairment. Algorithms for DR staging from digital fundus images
(DFIs) have been recently proposed. However, models often fail to generalize
due to distribution shifts between the source domain on which the model was
trained and the target domain where it is deployed. A common and particularly
challenging shift is often encountered when the source- and target-domain
supports do not fully overlap. In this research, we introduce DRStageNet, a
deep learning model designed to mitigate this challenge. We used seven publicly
available datasets, comprising a total of 93,534 DFIs that cover a variety of
patient demographics, ethnicities, geographic origins and comorbidities. We
fine-tune DINOv2, a pretrained model of self-supervised vision transformer, and
implement a multi-source domain fine-tuning strategy to enhance generalization
performance. We benchmark and demonstrate the superiority of our method to two
state-of-the-art benchmarks, including a recently published foundation model.
We adapted the grad-rollout method to our regression task in order to provide
high-resolution explainability heatmaps. The error analysis showed that 59\% of
the main errors had incorrect reference labels. DRStageNet is accessible at URL
[upon acceptance of the manuscript].