论文标题
Deep-OCTA：八八颗图像的糖尿病性视网膜病变分析的集合深学习方法
Deep-OCTA: Ensemble Deep Learning Approaches for Diabetic Retinopathy Analysis on OCTA Images
论文作者
论文摘要
超宽的光学相干断层扫描（OCTA）已成为糖尿病性视网膜病（DR）诊断的重要成像方式。但是，很少有研究重点是使用超宽八颗八颗自动DR分析。在本文中，我们介绍了基于糖尿病性视网膜病变分析挑战（DRAC）的超宽八章的新颖而实用的深度学习解决方案。在分割DR病变任务时，我们利用UNET和UNET ++来分割具有强大数据增强和模型集合的三个病变。在图像质量评估任务中，我们创建了InceptionV3，Se-Resnext和Vision Transformer模型的合奏。在大型数据集以及混合混合和CutMix策略上进行预训练都采用了我们的模型的概括能力。在DR分级任务中，我们构建了视觉变压器（VIT）和FND，该VIT模型预先训练在Color Fellus图像上可以用作八颗图像的有用底物。我们提出的方法分别在DRAC的三个排行榜上排名第四，第三和第五。源代码将在https://github.com/fdu-vts/drac上提供。
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.
