  The ultra-wide optical coherence tomography angiography (OCTA) has become an
important imaging modality in diabetic retinopathy (DR) diagnosis. However,
there are few researches focusing on automatic DR analysis using ultra-wide
OCTA. In this paper, we present novel and practical deep-learning solutions
based on ultra-wide OCTA for the Diabetic Retinopathy Analysis Challenge
(DRAC). In the segmentation of DR lesions task, we utilize UNet and UNet++ to
segment three lesions with strong data augmentation and model ensemble. In the
image quality assessment task, we create an ensemble of InceptionV3,
SE-ResNeXt, and Vision Transformer models. Pre-training on the large dataset as
well as the hybrid MixUp and CutMix strategy are both adopted to boost the
generalization ability of our model. In the DR grading task, we build a Vision
Transformer (ViT) and fnd that the ViT model pre-trained on color fundus images
serves as a useful substrate for OCTA images. Our proposed methods ranked 4th,
3rd, and 5th on the three leaderboards of DRAC, respectively. The source code
will be made available at https://github.com/FDU-VTS/DRAC.
