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

利用数字视网膜造影技术开发用于筛查和评估糖尿病及其并发症的自动算法，可以降低视力损害的风险和疾病并发症的严重程度。随着糖尿病的**患病率，影响全球超过4亿人，以及该疾病的长期微/宏观血管并发症，可能严重影响肾脏，心脏，神经系统和眼睛，迫切需要快速，准确和特异性的测试。该项目侧重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，**保健专业人员通过视觉检查视网膜图像来检测和分级DR.然而，疾病的压倒性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。为了提高不同可能治疗方法的成功机会，尽早客观地识别和分级DR至关重要。这就是最先进的人工智能算法，如深度卷积神经网络（cnn），可以利用大规模的视网膜图像数据集产生重大影响的地方。本课题的总体目标是设计一种全自动、高效、准确的视网膜图像DR检测和分级算法。我们打算解决一个4级问题，根据临床标准使用4个级别的严重程度**：轻度、中度、严重和增殖性。重点将放在最先进的深度CNN方法上。具体的子目标是：(1)研究一种新的半监督学习函数，**嵌入与问题相关的先验，利用大量未标记的图像，并**缓解由人类专家注释的训练集的有限大小；(2)设计、实现并**测试一个考虑视网膜图像特定背景的CNN架构；(3)通过将结果与真值注释进行比较，对**算法进行评价。

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.