Zhang Jing, Chen Juan
Jing Zhang, Department of Ophthalmology, Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, China.
Juan Chen, Department of Ophthalmology, Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, China.
Pak J Med Sci. 2025 Jan;41(1):225-229. doi: 10.12669/pjms.41.1.9171.
To design a deep learning-based model for early screening of diabetic retinopathy, predict the condition, and provide interpretable justifications.
The experiment's model structure is designed based on the Vision Transformer architecture which was initiated in March 2023 and the first version was produced in July 2023 at Affiliated Hospital of Hangzhou Normal University. We use the publicly available EyePACS dataset as input to train the model. Using the trained model, we predict whether a given patient's fundus images indicate diabetic retinopathy and provide the relevant affected areas as the basis for the judgement.
The model was validated using two subsets of the IDRiD dataset. Our model not only achieved good results in terms of detection accuracy, reaching around 0.88, but also performed comparably to similar models annotated for affected areas in predicting the affected regions.
Utilizing image-level annotations, we implemented a method for detecting diabetic retinopathy through deep learning and provided interpretable justifications to assist clinicians in diagnosis.
设计一种基于深度学习的糖尿病视网膜病变早期筛查模型，预测病情并提供可解释的依据。
实验模型结构基于2023年3月启动、2023年7月在杭州师范大学附属医院产生首个版本的视觉Transformer架构设计。我们使用公开可用的EyePACS数据集作为输入来训练模型。使用训练好的模型，我们预测给定患者的眼底图像是否表明患有糖尿病视网膜病变，并提供相关受影响区域作为判断依据。
该模型使用IDRiD数据集的两个子集进行了验证。我们的模型不仅在检测准确率方面取得了良好结果，达到约0.88，而且在预测受影响区域方面与标注了受影响区域的类似模型表现相当。
利用图像级注释，我们实现了一种通过深度学习检测糖尿病视网膜病变的方法，并提供可解释的依据以协助临床医生进行诊断。
