近年来，深度学习在计算机视觉方面取得了巨大的进步，并在利用计算机视觉完成医学影像的阅片工作方面展现出了良好的应用前景.针对糖尿病眼底病变筛查工作，通过构建两级深度卷积神经网络，完成了原始照片的特征提取、特征组合和结果分类，最终得出筛查结果.通过与医生的诊断结果进行比较，证明了模型的输出结果与医生诊断结果之间具有高度的一致性.同时，提出了利用弱监督学习进行细粒度图像分类的改进方法.最后，对未来研究的方向进行了展望.

In recent years, deep learning in the computer vision has made great progress, showing good application prospects in medical image reading. In this paper, a model with construction of two-level deep convolution neural network is designed to achieve feature extraction, feature blend, and classification of the fundus photo. By comparing with doctor's diagnosis, it is shown that the output of the model is highly consistent with the doctor's diagnosis. In addition, an improved method of fine-grained image classification using weak supervised learning is proposed. Finally, future research direction is discussed.

国家重点研发计划（2016YFF0201003）