Yao Zhaomin, Yuan Yizhe, Shi Zhenning, Mao Wenxin, Zhu Gancheng, Zhang Guoxu, Wang Zhiguo
College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, China.
Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning, China.
Front Physiol. 2022 Jul 25;13:961386. doi: 10.3389/fphys.2022.961386. eCollection 2022.
Diabetic retinopathy (DR) and age-related macular degeneration (AMD) are forms of degenerative retinal disorders that may result in vision impairment or even permanent blindness. Early detection of these conditions is essential to maintaining a patient's quality of life. The fundus photography technique is non-invasive, safe, and rapid way of assessing the function of the retina. It is widely used as a diagnostic tool for patients who suffer from fundus-related diseases. Using fundus images to analyze these two diseases is a challenging exercise, since there are rarely obvious features in the images during the incipient stages of the disease. In order to deal with these issues, we have proposed a deep learning method called FunSwin. The Swin Transformer constitutes the main framework for this method. Additionally, due to the characteristics of medical images, such as their small number and relatively fixed structure, transfer learning strategy that are able to increase the low-level characteristics of the model as well as data enhancement strategy to balance the data are integrated. Experiments have demonstrated that the proposed method outperforms other state-of-the-art approaches in both binary and multiclass classification tasks on the benchmark dataset.
糖尿病性视网膜病变（DR）和年龄相关性黄斑变性（AMD）是视网膜退行性疾病的两种形式，可能导致视力受损甚至永久性失明。早期发现这些病症对于维持患者的生活质量至关重要。眼底摄影技术是一种评估视网膜功能的非侵入性、安全且快速的方法。它被广泛用作患有眼底相关疾病患者的诊断工具。利用眼底图像分析这两种疾病是一项具有挑战性的工作，因为在疾病初期图像中很少有明显特征。为了解决这些问题，我们提出了一种名为FunSwin的深度学习方法。Swin Transformer构成了该方法的主要框架。此外，由于医学图像数量少且结构相对固定等特点，集成了能够增加模型低级特征的迁移学习策略以及平衡数据的数据增强策略。实验表明，在基准数据集上的二分类和多分类任务中，所提出的方法优于其他现有最先进的方法。
