Automated detection and grading of Diabetic Retinopathy using deep convolutional neural networks

利用数字视网膜造影术开发用于筛查和评估糖尿病及其并发症的自动化算法，可以降低视力受损的风险和疾病并发症的严重程度。糖尿病的流行影响着全球超过4亿人，而这种疾病的长期微观/宏观血管并发症可能会严重影响肾脏、心脏、神经系统和眼睛，因此迫切需要快速、准确和特异的检测。该项目专注于糖尿病视网膜病变(DR)的自动化和**早期检测/分级，这是该疾病**最常见和最严重的并发症之一，也是导致工作年龄患者失明的主要原因。在目前的实践中，**医疗保健专业人员通过视觉检查视网膜图像来检测和评级医生。然而，压倒性的**疾病流行和有限的合格专业人员给**视网膜专家带来了巨大的负担。此外，人类对这些图像的检查是主观的。为了提高不同可能的治疗方法的成功几率，关键是要以**客观的方式尽早识别并对DR进行分级。这就是最先进的人工智能算法，如深度卷积**神经网络(CNN)，可以利用大规模视网膜图像数据集产生重大影响的地方。这个项目的总体目标是设计一种全自动、高效和准确的算法来检测**视网膜图像中的DR并对其分级。我们打算解决一个4级问题，它使用4个严重程度**遵循临床标准：轻度、中度、重度和增殖性。重点将放在最先进的CNN深度方法上。具体的子目标是：(1)研究一种新的半监督学习函数，**它嵌入与问题相关的先验知识，利用大量的非标记图像，并**减轻由人类专家注释的训练集的有限大小；(2)设计、实现和**测试考虑视网膜图像的特定背景的CNN架构；以及(3)通过将结果与地面事实注释进行比较来评估**算法。

Developing automated algorithms for screening and assessing diabetes and its complications using digital**retinography can reduce the risks of visual impairment and the severity of disease complications. With the**prevalence of diabetes, which affects more than 400 million people worldwide, and the long-term micro/macro**vascular complications of the disease, which may affect seriously the kidneys, the heart, the nervous system**and the eyes, there is an urgent need of rapid, accurate and specific tests. This project focuses on automated and**early detection/grading of Diabetic Retinopathy (DR), one of the most frequent and serious complications of**the disease and the leading the cause of blindness amongst working-aged patients. In current practices,**health-care professionals inspect retinal images visually to detect and grade DR. However, the overwhelming**prevalence of the disease and the limited number of qualified professionals impose an enormous burden on**retina specialists. Furthermore, human inspection of these images is subjective. To enhance the chances of**success of different possible treatments, it is critical to identify and grade DR as early as possible, and in an**objective manner. This is where state-of-the-art artificial intelligence algorithms, such as deep convolutional**neural networks (CNNs), can make a significant impact, leveraging large-scale data sets of retinal images. The**overall objective of this project is to design a fully automated, efficient and accurate algorithm for detecting**and grading DR in retinal images. We intend to tackle a 4-class problem, which uses 4 levels of severity**following a clinical standard: mild, moderate, severe and proliferative. The focus will be on state-of-the-art**deep CNN approaches. Specific sub-objectives are: (1) investigating a novel semi-supervised learning function,**which embeds priors that are relevant to the problem, leveraging large sets of non-labeled images and**mitigating the limited sizes of training sets annotated by human experts; (2) designing, implementing and**testing a CNN architecture that accounts for the specific context of retinal images; and (3) evaluating the**algorithm by comparing the results to ground-truth annotations.