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- W4386213629 abstract "An important tool for effectively preventing blindness from eye diseases was an early screening of ophthalmic diseases. Eye fundus image scanning was the most cost-effective and rapid way to screen for ophthalmic diseases. The lack of medical resources and the inefficiency of diagnosis have been effectively improved using deep learning in eye fundus image screening. However, the detection effectiveness of the model in different scenarios could be poor due to the domain gap caused by medical equipment, shooting angle, etc. This paper proposed a scheme for model training enhancement through intermediate domains. Firstly, a residual neural network was utilized for a multi-classification task for fundus multi-disease detection. Secondly, CycleGAN was used as a classical-style migration generative network to generate target-like source domains. Finally, the intermediate domain was used to augment the source domain to train the deep residual network, thus implementing a UDA scheme. Experimental validation on two different domain datasets, On-site and Off-site, showed improved test results after augmentation with the intermediate domain. On the On-site dataset, the F1 Score and AUC improved by 2.72% and 2.94%, respectively. On the Off-site dataset, the F1 score and AUC improved by 4.32% and 6.78%, respectively. The experiments showed that the proposed scheme in this paper could effectively reduce the domain gap's impact and improve the model's generalization ability." @default.
- W4386213629 created "2023-08-29" @default.
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- W4386213629 date "2023-08-28" @default.
- W4386213629 modified "2023-09-30" @default.
- W4386213629 title "Domain adaptive multi-disease ocular disease recognition" @default.
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- W4386213629 doi "https://doi.org/10.1117/12.2687738" @default.
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