[1] V. S. E. Jeganathan, S.-M. Saw, T.-Y. Wong. Ocular morbidity of pathological myopia. Myopia, 97-120(2010).

[2] B. A. Holden, T. R. Fricke, D. A. Wilson, M. Jong, K. S. Naidoo, P. Sankaridurg et al. Global prevalence of myopia and high myopia and temporal trends from 2000 through 2050. Ophthalmology, 123, 1036-1042(2016). https://doi.org/10.1016/j.ophtha.2016.01.006

[3] Y. Tang, X. Wang, J. Wang, W. Huang, Y. Gao, Y. Luo, Y. Lu. Prevalence and causes of visual impairment in a Chinese adult population: The Taizhou eye study. Ophthalmology, 122, 1480-1488(2015). https://doi.org/10.1016/j.ophtha.2015.03.022

[4] K. Ohno-Matsui, R. Kawasaki, J. B. Jonas, C. M. Cheung, S. M. Saw, V. J. Verhoeven et al. International photographic classification and grading system for myopic maculopathy. Am. J. Ophthalmol., 159, 877-83.e7(2015). https://doi.org/10.1016/j.ajo.2015.01.022

[5] T. Sherman. Fundus photographs and other fundus imaging. Fundamentals of Ophthalmic Practice, 229-237(2023).

[6] W. Zhang, X. Zhao, Y. Chen, J. Zhong, Z. Yi. DeepUWF: An automated ultrawide-field fundus screening system via deep learning. IEEE J. Biomed. Health Informatics, 25, 2988-2996(2021).

[7] Y. Tang, Y. Xu. Diabetic retinopathy analysis based on retinal fundus photographs via deep learning. Advances in Pattern Recognition and Artificial Intelligence, 61-76(2021).

[8] Z. Gao, J. Li, J. Guo, Y. Chen, Z. Yi, J. Zhong. Diagnosis of diabetic retinopathy using deep neural networks. IEEE Access, 7, 3360-3370(2019).

[9] R. Du, S. Xie, Y. Fang, T. Igarashi-Yokoi, M. Moriyama, S. Ogata et al. Deep learning approach for automated detection of myopic maculopathy and pathologic myopia in fundus images. Ophthalmol. Retina, 5, 1235-1244(2021). https://doi.org/10.1016/j.oret.2021.02.006

[10] J. Tang, M. Yuan, K. Tian, Y. Wang, D. Wang, J. Yang et al. An artificial-intelligence-based automated grading and lesions segmentation system for myopic maculopathy based on color fundus photographs. Transl. Vis. Sci. Technol., 11, 16(2022). https://doi.org/10.1167/tvst.11.6.16

[11] Z. Wu, W. Cai, H. Xie, S. Chen, Y. Wang, B. Lei et al. Predicting optical coherence tomography-derived high myopia grades from fundus photographs using deep learning. Front. Med. (Lausanne), 9, 842680(2022). https://doi.org/10.3389/fmed.2022.842680

[12] L. Lu, P. Ren, X. Tang, M. Yang, M. Yuan, W. Yu et al. AI-model for identifying pathologic myopia based on deep learning algorithms of myopic maculopathy classification and “plus” lesion detection in fundus images. Front. Cell Dev. Biol., 9, 719262(2021). https://doi.org/10.3389/fcell.2021.719262

[13] J. Li, L. Wang, Y. Gao, Q. Liang, L. Chen, X. Sun et al. Automated detection of myopic maculopathy from color fundus photographs using deep convolutional neural networks. Eye Vis. (Lond.), 9, 13(2022). https://doi.org/10.1186/s40662-022-00285-3

[14] K. J. Choi, J. E. Choi, H. C. Roh, J. S. Eun, J. M. Kim, Y. K. Shin et al. Deep learning models for screening of high myopia using optical coherence tomography. Sci. Rep., 11, 21663(2021). https://doi.org/10.1038/s41598-021-00622-x

[15] R. Wang, J. He, Q. Chen, L. Ye, D. Sun, L. Yin et al. Efficacy of a deep learning system for screening myopic maculopathy based on color fundus photographs. Ophthalmol. Ther., 12, 469-484(2023). https://doi.org/10.1007/s40123-022-00621-9

[16] K. He, X. Zhang, S. Ren, J. Sun. Deep residual learning for image recognition. Proc. IEEE Conf. Computer Vision and Pattern Recognition, 770-778(2016).

[17] Y. Pan, J. Liu, Y. Cai, X. Yang, Z. Zhang, H. Long et al. Fundus image classification using Inception V3 and ResNet-50 for the early diagnostics of fundus diseases. Front. Physiol., 14, 1126780(2023).

[18] N. Chea, Y. Nam. Classification of fundus images based on deep learning for detecting eye diseases. Comput., Mater. Continua, 67, 411-426(2021).

[19] R. Benzi, A. Sutera, A. Vulpiani. The mechanism of stochastic resonance. J. Phys. A: Math. Gen., 14, L453(1999). https://doi.org/10.1088/0305-4470/14/11/006

[20] L. Pereira, Y. Taya, I. Kobayashi. Multi-layer random perturbation training for improving model generalization efficiently. Proc. Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, 303-310(2021).

[21] Yu X., Xue Y., Zhang L., Wang L., Liu T., Zhu D.. Exploring the influence of information entropy change in learning systems(2023).

[22] Y. Shao, S. C. Liew, D. Gündüz. Denoising noisy neural networks: A bayesian approach with compensation. IEEE Trans. Signal Process., 71, 2460-2474(2023).

[23] Simonyan K., Zisserman A.. Very deep convolutional networks for large-scale image recognition(2014).

[24] Dosovitskiy A., Beyer L., Kolesnikov A., Weissenborn D., Zhai X., Unterthiner T. et al. An image is worth 16×16 words: Transformers for image recognition at scale(2020).

[25] Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang et al. Swin transformer: Hierarchical vision transformer using shifted windows. Proc. IEEE/CVF Int. Conf. Computer Vision 2021, 10012-10022(2021).

[26] M. Tan, Q. Le. Efficientnet: Rethinking model scaling for convolutional neural networks. Int. Conf. Machine Learning: PMLR, 6105-6114(2019).