Diabetic retinopathy(DR)is one of the most common complications of diabetes.With the development of the disease,patients will have different degrees of retinopathy,so timely diagnosis of DR is very necessary.Fluorescein Angiography Images(FFA)and Fundus fluorescein angiography images(FFA)are complementary in the diagnosis of DR,but they can not obtain the multi-modal image information required for diagnosis in time due to limited medical resources and the invasive nature of FFA.Based on the idea of unsupervised networks,a new cross-modal fundus image conversion network-NUFGAN(A generative adversarial network combining negative samples,Unet and fused attention,NUFGAN)is proposed.Thus,multi-modal images under the requirement of diagnosis can be obtained.To solve the problems in the transformation,new negative sample constraint and negative sample loss function are added to NUFGAN network,the generator structure is modified and the clustering algorithm is added.Based on the comparative experiment on the effectiveness of NUFGAN network,the results show that the conversion results of NUFGAN are compared with those of CycleGAN,NiceGAN,U-GAT-IT-light and PGGAN four advanced unsupervised networks.It is higher by 1.21,0.28,0.24 and 0.90 in PSNR and 1.61%,0.71%,0.17%and 4.47%in SSIM,respectively.Based on the study of the validity of multimodal data as input and the further study of the validity of NUFGAN,the study conducts the downstream DR classification experiment.The experimental data show that the results of unsupervised DR classification network with multi-modal data as input are 1.78%,0.27%,1.13%and 1.10%higher in accuracy,precision,recall and F1-Score,respectively,than those of unsupervised DR classification network with single-modal data as input.
关键词
深度学习/糖尿病视网膜病变/无监督网络/跨模态转换
Key words
Deep learning/Diabetic retinopathy/Unsupervised network/Cross-modal conversion
维普
万方数据