无源多领域自适应糖尿病视网膜病变分类方法

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关键词：糖尿病视网膜病变；深度学习；无源多领域自适应；扩散域注意力迁移学习 中图分类号：TP18文献标识码：A doi：10.37188/CJLCD.2025-0024 CSTR：32172.14.CJLCD.2025-0024

Abstract： For diagnosis of diabetic retinopathy based on domain adaptation methods in deep learning，the difusion-enhanced domain-atention transfer learning model proposed in this paper consists of two main modules. Firstly，the denoising diffusion probabilistic diabetic retinopathy generation module generates abundant and diverse target domain samples，enabling the model to learn more comprehensive target domain features. Secondly，our model designs a multi-source-free attention ensemble module，which achieves weighted attention integration of multiple source domain pre-trained models，without the need to access source domain data.Therefore，this model obtains a good balance between instance-specific features and domain-consistent features.Experimental results demonstrate that the model achieves an accuracy of 90.66% ，a precision of 87.47% ，a sensitivity of 85.41% ，a specificity of 91.63% ，and an F1 score of

86.42% in the referable diabetic retinopathy diagnosis task．Meanwhile，in the normal/abnormal retinopathy recognition task，the model reaches an accuracy of 96.75% ，a precision of 99.23% ，a sensitivity of 90.47% ，a specificity of 99. 27% ，and an Fl score of 94.65% . The model proposed in this paper can conduct effective retinopathy diagnosis without accessing source domain data and without target domain labels.

Key words： diabetic retinopathy；deep learning； source-free multi-domain adaptive；difusion-enhanced domain-attention transfer learning

1引言

糖尿病视网膜病变（DiabeticRetinopathy，DR）作为糖尿病的主要并发症之一，其患病率在全球范围内日益增长[1]。（剩余16987字）