  Diabetic retinopathy (DR) is a complication of diabetes, and one of the major
causes of vision impairment in the global population. As the early-stage
manifestation of DR is usually very mild and hard to detect, an accurate
diagnosis via eye-screening is clinically important to prevent vision loss at
later stages. In this work, we propose an ensemble method to automatically
grade DR using ultra-wide optical coherence tomography angiography (UW-OCTA)
images available from Diabetic Retinopathy Analysis Challenge (DRAC) 2022.
First, we adopt the state-of-the-art classification networks, i.e., ResNet,
DenseNet, EfficientNet, and VGG, and train them to grade UW-OCTA images with
different splits of the available dataset. Ultimately, we obtain 25 models, of
which, the top 16 models are selected and ensembled to generate the final
predictions. During the training process, we also investigate the multi-task
learning strategy, and add an auxiliary classification task, the Image Quality
Assessment, to improve the model performance. Our final ensemble model achieved
a quadratic weighted kappa (QWK) of 0.9346 and an Area Under Curve (AUC) of
0.9766 on the internal testing dataset, and the QWK of 0.839 and the AUC of
0.8978 on the DRAC challenge testing dataset.
