提出GreenBen增强方法，提升糖尿病视网膜病变图像分类准确率10%。
Diabetic retinopathy image classification method based on GreenBen data augmentation
- 用绿通道图像做本征增强，构建新数据增广方法GreenBen。
- 在三数据集上实现最高精度，联合诊断DR与黄斑水肿时准确率提升10%。
- 适合医学图像分类研究者，尤其关注数据增强与多任务学习的场景。
针对糖尿病视网膜病变（DR）图像诊断，本文提出一种基于人工智能的分类方法。核心是提出一种新型数据增强方法GreenBen：先从视网膜图像提取绿通道灰度图，再进行本征增强。考虑到糖尿病性黄斑水肿（DME）与DR密切相关，本文构建基于多任务学习与注意力模块的联合分类框架，并利用GreenBen增强数据以缩小图像差异，提升模型分类准确性。在三个公开数据集上开展大量实验，结果表明，无论采用ResNet50或Swin Transformer网络，无论是单独分类还是联合DME分类，GreenBen均显著优于其他数据增强方法，在DR分类中实现稳定且显著的性能提升，准确率提高10%。
原文摘要 · Abstract (English)
For the diagnosis of diabetes retinopathy (DR) images, this paper proposes a classification method based on artificial intelligence. The core lies in a new data augmentation method, GreenBen, which first extracts the green channel grayscale image from the retinal image and then performs Ben enhancement. Considering that diabetes macular edema (DME) is a complication closely related to DR, this paper constructs a joint classification framework of DR and DME based on multi task learning and attention module, and uses GreenBen to enhance its data to reduce the difference of DR images and improve the accuracy of model classification. We conducted extensive experiments on three publicly available datasets, and our method achieved the best results. For GreenBen, whether based on the ResNet50 network or the Swin Transformer network, whether for individual classification or joint DME classification, compared with other data augmentation methods, GreenBen achieved stable and significant improvements in DR classification results, with an accuracy increase of 10%.
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