- 1、Ye, Y., Mao J., Liu L., Zhang S., Shen L., & Sun, M.* (2020). Automatic Diagnosis of Familial Exudative Vitreoretinopathy Using a Fusion Neural Network for Wide-Angle Retinal Images. IEEE Access, 8, 162-173.
- 2、Wang, X., Zhang, S., Liang, X., Zhou, H., Zheng, J., & Sun, M.* (2019). Accurate and Fast Blur Detection Using a Pyramid M-Shaped Deep Neural Network. IEEE Access, 7, 86611–86624.
- 3、Mao, J., Luo, Y., Chen, K., Lao, J., Chen, L., Shao, Y., Zhang, C., Sun, M.*, & Shen, L.*(2019). New grading criterion for retinal hemorrhages in term newborns based on deep convolutional neural networks. Clinical & Experimental Ophthalmology.
- 4、Mao, J., Luo, Y., Liu, L., Lao, J., Shao, Y., Zhang, M., Zhang, C., Sun, M.*, &Shen, L.* (2019). Automated diagnosis and quantitative analysis of plus disease in retinopathy of prematurity based on deep convolutional neural networks. Acta Ophthalmologica.
- 5、Li, Y., Zheng, R., Wu, Y., Chu, K., Xu, Q., Sun, M.*, & Smith, Z. J.* (2019). A low‐cost, automated parasite diagnostic system via a portable, robotic microscope and deep learning. Journal of Biophotonics, e201800410.
- 6、Wang, X., Zhang, S., Liang, X., Zheng, C., Zheng, J., & Sun, M.* (2019). A CNN-based retinal image quality assessment system for teleophthalmology. Journal of Mechanics in Medicine and Biology, 19(05), 1950030.
- 7、Chen, H., Liu, G., Zhang, S., Shen, S., Shen,S., Luo, Y., Li, J., Roberts,C.J., Sun,M. *, & Xu,R.X.*(2019). Fundus-simulating phantom for calibration of retinal vessel oximetry devices. Applied Optics, 58(14), 3877-3885.
- 8、Zhang, S., Zheng, R., Luo, Y., Wang, X., Mao, J., Roberts, C. J., & Sun, M. *(2019). Simultaneous Arteriole and Venule Segmentation of Dual-Modal Fundus Images Using a Multi-Task Cascade Network. IEEE Access, 7, 57561-57573.
- 9、Liu, L., Luo, Y., Shen, X., Sun, M., & Li, B. (2019). β-dropout: a Unified Dropout. IEEE Access.
- 10、Zheng, R., Liu, L., Zhang, S., Zheng, C., Bunyak, F., Xu, R., Li,B., & Sun, M.* (2018). Detection of exudates in fundus photographs with imbalanced learning using conditional generative adversarial network. Biomedical optics express, 9(10), 4863-4878.