Using Deep Learning and Transfer Learning to Accurately Diagnose Early-Onset Glaucoma From Macular Optical Coherence Tomography Images.

[1]  W. Youden,et al.  Index for rating diagnostic tests , 1950, Cancer.

[2]  S. Holm A Simple Sequentially Rejective Multiple Test Procedure , 1979 .

[3]  D. Apple,et al.  Congenital anomalies of the optic disc. , 1982, Survey of ophthalmology.

[4]  M. Bruce Shields,et al.  Textbook of glaucoma , 1987 .

[5]  E. DeLong,et al.  Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. , 1988, Biometrics.

[6]  Douglas R. Anderson Automated Static Perimetry , 1992 .

[7]  R. Weinreb,et al.  Mechanisms of optic nerve damage in primary open angle glaucoma. , 1994, Survey of ophthalmology.

[8]  M. Aickin,et al.  Adjusting for multiple testing when reporting research results: the Bonferroni vs Holm methods. , 1996, American journal of public health.

[9]  Thomas G. Dietterich Adaptive computation and machine learning , 1998 .

[10]  B. Bengtsson,et al.  False-negative responses in glaucoma perimetry: indicators of patient performance or test reliability? , 2000, American journal of ophthalmology.

[11]  T. Zimmerman,et al.  Clinical Pathways in Glaucoma , 2001 .

[12]  M. Segal,et al.  Relating Amino Acid Sequence to Phenotype: Analysis of Peptide‐Binding Data , 2000, Biometrics.

[13]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[14]  P. Khaw,et al.  Primary open-angle glaucoma , 2004, The Lancet.

[15]  K. Lunetta,et al.  Screening large-scale association study data: exploiting interactions using random forests , 2004, BMC Genetics.

[16]  C. Glymour,et al.  Optical coherence tomography machine learning classifiers for glaucoma detection: a preliminary study. , 2005, Investigative ophthalmology & visual science.

[17]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[18]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[19]  H. Quigley,et al.  The number of people with glaucoma worldwide in 2010 and 2020 , 2006, British Journal of Ophthalmology.

[20]  Marc'Aurelio Ranzato,et al.  Sparse Feature Learning for Deep Belief Networks , 2007, NIPS.

[21]  Achim Zeileis,et al.  BMC Bioinformatics BioMed Central Methodology article Conditional variable importance for random forests , 2008 .

[22]  Yoshua Bengio,et al.  Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.

[23]  G. Wollstein,et al.  Detection of macular ganglion cell loss in glaucoma by Fourier-domain optical coherence tomography. , 2009, Ophthalmology.

[24]  Honglak Lee,et al.  Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations , 2009, ICML '09.

[25]  I. Schmidtmann,et al.  Diagnostic ability of retinal ganglion cell complex, retinal nerve fiber layer, and optic nerve head measurements by Fourier-domain optical coherence tomography , 2011, Graefe's Archive for Clinical and Experimental Ophthalmology.

[26]  Yann LeCun,et al.  Convolutional Learning of Spatio-temporal Features , 2010, ECCV.

[27]  Y-Lan Boureau,et al.  Learning Convolutional Feature Hierarchies for Visual Recognition , 2010, NIPS.

[28]  Robert N Weinreb,et al.  Comparison of different spectral domain optical coherence tomography scanning areas for glaucoma diagnosis. , 2010, Ophthalmology.

[29]  Eun Suk Lee,et al.  Structure-function relationship and diagnostic value of macular ganglion cell complex measurement using Fourier-domain OCT in glaucoma. , 2010, Investigative ophthalmology & visual science.

[30]  Robert N Weinreb,et al.  Comparison of the diagnostic accuracies of the Spectralis, Cirrus, and RTVue optical coherence tomography devices in glaucoma. , 2011, Ophthalmology.

[31]  M. T. Leite,et al.  Spectral-domain optical coherence tomography for early glaucoma assessment: analysis of macular ganglion cell complex versus peripapillary retinal nerve fiber layer. , 2011, Canadian journal of ophthalmology. Journal canadien d'ophtalmologie.

[32]  Tin Aung,et al.  Classification algorithms enhance the discrimination of glaucoma from normal eyes using high-definition optical coherence tomography. , 2012, Investigative ophthalmology & visual science.

[33]  Jianlin Cheng,et al.  DNdisorder: predicting protein disorder using boosting and deep networks , 2013, BMC Bioinformatics.

[34]  Robert N Weinreb,et al.  Comparison of different spectral domain OCT scanning protocols for diagnosing preperimetric glaucoma. , 2013, Investigative ophthalmology & visual science.

[35]  Jean-Claude Mwanza,et al.  Combining spectral domain optical coherence tomography structural parameters for the diagnosis of glaucoma with early visual field loss. , 2013, Investigative ophthalmology & visual science.

[36]  Yoshua Bengio,et al.  How transferable are features in deep neural networks? , 2014, NIPS.

[37]  Ryo Asaoka,et al.  Discriminating between Glaucoma and Normal Eyes Using Optical Coherence Tomography and the ‘Random Forests’ Classifier , 2014, PloS one.

[38]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[39]  Ronald M. Summers,et al.  Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation , 2015, IEEE Transactions on Medical Imaging.

[40]  Hiroshi Murata,et al.  Detecting Preperimetric Glaucoma with Standard Automated Perimetry Using a Deep Learning Classifier. , 2016, Ophthalmology.

[41]  Hoo-Chang Hoo-Chang Shin Shin,et al.  Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning , 2016, Ieee Transactions on Medical Imaging.

[42]  P. Lakhani,et al.  Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks. , 2017, Radiology.

[43]  Hiroshi Murata,et al.  Validating the Usefulness of the "Random Forests" Classifier to Diagnose Early Glaucoma With Optical Coherence Tomography. , 2017, American journal of ophthalmology.

[44]  Donald C. Hood,et al.  Improving our understanding, and detection, of glaucomatous damage: An approach based upon optical coherence tomography (OCT) , 2017, Progress in retinal and eye research.

[45]  Siamak Yousefi,et al.  Estimating Glaucomatous Visual Sensitivity from Retinal Thickness with Pattern-Based Regularization and Visualization , 2018, KDD.