Feasibility assessment of infectious keratitis depicted on slit-lamp and smartphone photographs using deep learning
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Jiantao Pu | Lei Wang | Wei Chen | Kuan Chen | Han Wen | Qinxiang Zheng | Yang Chen | J. Pu | Q. Zheng | Wei Chen | Yang Chen | Kuan Chen | Lei Wang | Han Wen
[1] M. Burton,et al. Distinguishing fungal and bacterial keratitis on clinical signs , 2015, Community eye health.
[2] Hao Chen,et al. Automated segmentation of the optic disc from fundus images using an asymmetric deep learning network , 2021, Pattern Recognit..
[3] T. Liesegang. Classification of herpes simplex virus keratitis and anterior uveitis. , 1999, Cornea.
[4] Jianqiang Li,et al. Exploiting ensemble learning for automatic cataract detection and grading , 2016, Comput. Methods Programs Biomed..
[5] L. E. Clemens,et al. Designed Host Defense Peptides for the Treatment of Bacterial Keratitis , 2017, Investigative ophthalmology & visual science.
[6] A. Roychoudhury,et al. Epithelial remodeling as basis for machine-based identification of keratoconus. , 2014, Investigative ophthalmology & visual science.
[7] Alexandru Lavric,et al. KeratoDetect: Keratoconus Detection Algorithm Using Convolutional Neural Networks , 2019, Comput. Intell. Neurosci..
[8] S. Sivaprasad,et al. Diagnostic Accuracy of Community-Based Diabetic Retinopathy Screening With an Offline Artificial Intelligence System on a Smartphone. , 2019, JAMA ophthalmology.
[9] Abhishek Das,et al. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).
[10] S. Kulkarni,et al. Herpes Simplex Virus: The Hostile Guest That Takes Over Your Home , 2020, Frontiers in Microbiology.
[11] Subhashini Venugopalan,et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. , 2016, JAMA.
[12] Aini Hussain,et al. Automated pterygium detection method of anterior segment photographed images , 2018, Comput. Methods Programs Biomed..
[13] Xinyi Wu,et al. Hyphae Detection in Fungal Keratitis Images With Adaptive Robust Binary Pattern , 2018, IEEE Access.
[14] Lizhen Cui,et al. Automatic diagnosis of fungal keratitis using data augmentation and image fusion with deep convolutional neural network , 2020, Comput. Methods Programs Biomed..
[15] Lamprini Papaioannou,et al. Corneal Collagen Cross-Linking for Infectious Keratitis: A Systematic Review and Meta-Analysis , 2016, Cornea.
[16] Z. Nagy,et al. Accuracy of machine learning classifiers using bilateral data from a Scheimpflug camera for identifying eyes with preclinical signs of keratoconus , 2016, Journal of cataract and refractive surgery.
[17] Saurabh Ghosh,et al. A 10-year analysis of microbiological profiles of microbial keratitis: the North East England Study , 2018, Eye.
[18] J. Rose-Nussbaumer,et al. Update on the Management of Infectious Keratitis. , 2017, Ophthalmology.
[19] N. Shoji,et al. Keratoconus detection using deep learning of colour-coded maps with anterior segment optical coherence tomography: a diagnostic accuracy study , 2019, BMJ Open.
[20] J. Mehta,et al. The Asia Cornea Society Infectious Keratitis Study: A Prospective Multicenter Study of Infectious Keratitis in Asia. , 2018, American journal of ophthalmology.
[21] Qiang Zhu,et al. Deep Sequential Feature Learning in Clinical Image Classification of Infectious Keratitis , 2020, Engineering.
[22] Michael W. Belin,et al. Enhanced Tomographic Assessment to Detect Corneal Ectasia Based on Artificial Intelligence. , 2018, American journal of ophthalmology.
[23] Sina Farsiu,et al. Novel Image-Based Analysis for Reduction of Clinician-Dependent Variability in Measurement of the Corneal Ulcer Size , 2017, Cornea.
[24] Carina Koppen,et al. Computer aided diagnosis for suspect keratoconus detection , 2019, Comput. Biol. Medicine.