论文标题
糖尿病性视网膜病检测的深度半监督和自我监督的学习
Deep Semi-Supervised and Self-Supervised Learning for Diabetic Retinopathy Detection
论文作者
论文摘要
糖尿病性视网膜病（DR）是发达国家工人衰老人群中失明的主要原因之一，这是由于糖尿病的副作用降低了视网膜的血液供应。深度神经网络已被广泛用于自动化系统中，以在眼底图像上进行DR分类。但是，这些模型需要大量带注释的图像。在医疗领域，专家的注释昂贵，乏味且耗时。结果，提供了有限数量的注释图像。本文提出了一种半监督的方法，该方法利用图像未标记的图像和标记图像来训练一种检测糖尿病性视网膜病的模型。提出的方法通过自我监督的学习使用无监督的预处理，然后使用一小组标记的图像和知识蒸馏来监督微调，以提高分类任务的性能。在Eyepacs测试和Messidor-2数据集上评估了该方法，仅使用2％的Eyepacs火车标记的图像来分别实现0.94和0.89 AUC。
Diabetic retinopathy (DR) is one of the leading causes of blindness in the working-age population of developed countries, caused by a side effect of diabetes that reduces the blood supply to the retina. Deep neural networks have been widely used in automated systems for DR classification on eye fundus images. However, these models need a large number of annotated images. In the medical domain, annotations from experts are costly, tedious, and time-consuming; as a result, a limited number of annotated images are available. This paper presents a semi-supervised method that leverages unlabeled images and labeled ones to train a model that detects diabetic retinopathy. The proposed method uses unsupervised pretraining via self-supervised learning followed by supervised fine-tuning with a small set of labeled images and knowledge distillation to increase the performance in classification task. This method was evaluated on the EyePACS test and Messidor-2 dataset achieving 0.94 and 0.89 AUC respectively using only 2% of EyePACS train labeled images.
