Hierarchical Features Integration and Attention Iteration Network for Juvenile Refractive Power Prediction

[1]  Wei-Wei Tu,et al.  AutoCross , 2019, Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.

[2]  Xiaohang Wu,et al.  Prediction of myopia development among Chinese school-aged children using refraction data from electronic medical records: A retrospective, multicentre machine learning study , 2018, PLoS medicine.

[3]  DeepGBM , 2019, Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.

[4]  D. Mutti,et al.  Refractive error, axial length, and relative peripheral refractive error before and after the onset of myopia. , 2007, Investigative ophthalmology & visual science.

[5]  R. Varma,et al.  Visual Impairment and Blindness in Adults in the United States: Demographic and Geographic Variations From 2015 to 2050. , 2016, JAMA ophthalmology.

[6]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[7]  Chen Wang,et al.  RAUNet: Residual Attention U-Net for Semantic Segmentation of Cataract Surgical Instruments , 2019, ICONIP.

[8]  N. Congdon,et al.  Validating the accuracy of a model to predict the onset of myopia in children. , 2011, Investigative ophthalmology & visual science.

[9]  Charu C. Aggarwal,et al.  Using a Random Forest to Inspire a Neural Network and Improving on It , 2017, SDM.

[10]  C. Klaver,et al.  Axial length growth and the risk of developing myopia in European children , 2017, Acta ophthalmologica.

[11]  J. Stack,et al.  Refractive error and visual impairment in Ireland schoolchildren , 2018, British Journal of Ophthalmology.

[12]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.