深度学习方法在糖尿病视网膜病变诊断中的应用

深度学习可以有效提取图像隐含特征,在医学影像识别方面的应用快速发展. 由于糖尿病视网膜病变(Diabetic retinopathy, DR)诊断标准明确、分类体系成熟,应用深度学习诊断糖尿病视网膜病变近年来成为研究热点. 本文从深度学习方法在DR诊断中的最新研究进展、DR诊断的一般流程、公共数据集、医学影像标注方法、主要实现模型、面临的主要挑战几方面, 对深度学习方法在糖尿病视网膜病变诊断中的应用进行了详细综述, 便于更多机器视觉、尤其是深度学习医学影像的研究者们参照对比,加快该领域研究的成熟度和临床落地应用.

Applications of Deep Learning Techniques for Diabetic Retinal Diagnosis

Deep learning can effectively extract the hidden features of image and its application in medical image recognition is developing rapidly. Due to the clear diagnostic criteria for diabetic retinopathy (DR) and the mature classification system, the application of deep learning to diagnose diabetic retinopathy has become a research hotspot in recent years. Therefore, this paper reviews the application of deep learning methods in the diagnosis of diabetic retinopathy detailedly based on the latest research progress of deep learning for DR diagnosis, the general flow for DR diagnosis, public dataset, medical image annotation method, main models and major challenges. It brings convenience for more researchers of computer vision deepling learning, especially medical imaging deep learning, to speed up the research maturity and clinical application in this field.