[1] LI G H. Correlations among blood lipid index and glycosylated hemoglobin levels and severity and prognosis of patients with diabetic retinopathy[J]. Medical Journal of Chinese People’s Health, 36, 149-152(2024).

[2] WOLF R M, CHANNA R, LIU T Y A et al. Autonomous artificial intelligence increases screening and follow-up for diabetic retinopathy in youth: the ACCESS randomized control trial[J]. Nature Communications, 15, 421(2024).

[3] YANG F Y, PENG S J, LIU J et al. To explore the potential biomarkers of proliferative diabetic retinopathy by plasma proteomics[J]. Journal of Capital Medical University, 45, 385-391(2024).

[4] GADEKALLU T R, KHARE N, BHATTACHARYA S et al. Deep neural networks to predict diabetic retinopathy[J]. Journal of Ambient Intelligence and Humanized Computing, 14, 5407-5420(2023).

[5] DAI L, SHENG B, CHEN T L et al. A deep learning system for predicting time to progression of diabetic retinopathy[J]. Nature Medicine, 30, 584-594(2024).

[6] ZHANG Z Q, ZHAO K H, NIU H F et al. Application of deep learning in grading of diabetic retinopathy[J]. Computer Systems & Applications, 33, 231-244(2024).

[7] YU X Y, LIU J X, XUE W X et al. Diabetic retinopathy image classification model for based on improved CNN[J]. Modern Electronics Technique, 44, 168-172(2021).

[8] GALAPPATHTHIGE C J, KURUPPU G, KHAN M H. Generalizing to unseen domains in diabetic retinopathy classification[C], 7685-7695(2024).

[9] ZHU J D, ZHUO G P, WU S Y et al. Classification and detection of diabetes retinopathy based on transfer learning[J]. Journal of North University of China (Natural Science Edition), 44, 478-486(2023).

[10] HO J, JAIN A, ABBEEL P. Denoising diffusion probabilistic models[C], 574(2020).

[11] KARTHIK M, SOHIER D. APTOS 2019 blindness detection[R](2019).

[12] LI T, GAO Y Q, WANG K et al. Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening[J]. Information Sciences, 501, 511-522(2019).

[13] PORWAL P, PACHADE S, KAMBLE R et al. Indian diabetic retinopathy image dataset (IDRiD): a database for diabetic retinopathy screening research[J]. Data, 3, 25(2018).

[14] DECENCIÈRE E, ZHANG X W, CAZUGUEL G et al. Feedback on a publicly distributed image database: the messidor database[J]. Image Analysis and Stereology, 33, 231-234(2014).

[15] CUADROS J, BRESNICK G. EyePACS: an adaptable telemedicine system for diabetic retinopathy screening[J]. Journal of Diabetes Science and Technology, 3, 509-516(2009).

[16] HE K M, ZHANG X Y, REN S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

[17] MURPHY K, SCHÖLKOPF B, GANIN Y et al. Domain-adversarial training of neural networks[J]. The Journal of Machine Learning Research, 17, 2096-2030(2016).

[18] LONG M S, ZHU H, WANG J M et al. Deep transfer learning with joint adaptation networks[C], 2208-2217(2017).

[19] TZENG E, HOFFMAN J, SAENKO K et al. Adversarial discriminative domain adaptation[C], 7167-7176(2017).

[20] LONG M S, CAO Z J, WANG J M et al. Conditional adversarial domain adaptation[C], 1647-1657(2018).