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

Deep-OCTA: Ensemble Deep Learning Approaches for Diabetic Retinopathy Analysis on OCTA Images