A hierarchical deep learning approach with transparency and interpretability based on small samples for glaucoma diagnosis

[1]  The Convergence of Human and Artificial Intelligence on Clinical Care - Part I , 2022 .

[2]  Robert N Weinreb,et al.  Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs. , 2019, JAMA ophthalmology.

[3]  Eric J Topol,et al.  High-performance medicine: the convergence of human and artificial intelligence , 2019, Nature Medicine.

[4]  Jie Xu,et al.  The practical implementation of artificial intelligence technologies in medicine , 2019, Nature Medicine.

[5]  Christopher Bowd,et al.  Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs , 2018, Scientific Reports.

[6]  K. Mori,et al.  Real-Time Use of Artificial Intelligence in Identification of Diminutive Polyps During Colonoscopy , 2018, Annals of Internal Medicine.

[7]  Marcus A. Badgeley,et al.  Automated deep-neural-network surveillance of cranial images for acute neurologic events , 2018, Nature Medicine.

[8]  G. Rosman,et al.  Artificial Intelligence in Surgery: Promises and Perils , 2018, Annals of surgery.

[9]  A. Peters,et al.  A Deep Learning Algorithm for Prediction of Age-Related Eye Disease Study Severity Scale for Age-Related Macular Degeneration from Color Fundus Photography. , 2018, Ophthalmology.

[10]  M. He,et al.  Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs. , 2018, Ophthalmology.

[11]  Daniel S. Kermany,et al.  Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning , 2018, Cell.

[12]  Xiaochun Cao,et al.  Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation , 2018, IEEE Transactions on Medical Imaging.

[13]  Andrew H. Beck,et al.  Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer , 2017, JAMA.

[14]  E. Finkelstein,et al.  Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes , 2017, JAMA.

[15]  Neil J. Joshi,et al.  Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks , 2017, JAMA ophthalmology.

[16]  Gregory S. Corrado,et al.  Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning , 2017, Nature Biomedical Engineering.

[17]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[18]  Subhashini Venugopalan,et al.  Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. , 2016, JAMA.

[19]  Tien Yin Wong,et al.  Artificial Intelligence With Deep Learning Technology Looks Into Diabetic Retinopathy Screening. , 2016, JAMA.

[20]  Bo Wang,et al.  When Ensemble Learning Meets Deep Learning: a New Deep Support Vector Machine for Classification , 2016, Knowl. Based Syst..

[21]  O. Stegle,et al.  Deep learning for computational biology , 2016, Molecular systems biology.

[22]  D. Shen,et al.  Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans , 2016, Scientific Reports.

[23]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[25]  David Sussillo,et al.  Opening the Black Box: Low-Dimensional Dynamics in High-Dimensional Recurrent Neural Networks , 2013, Neural Computation.

[26]  Geoffrey I. Webb,et al.  Encyclopedia of Machine Learning , 2011, Encyclopedia of Machine Learning.

[27]  Robert Ritch,et al.  The ISNT rule and differentiation of normal from glaucomatous eyes. , 2006, Archives of ophthalmology.

[28]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[29]  David A. Landgrebe,et al.  A survey of decision tree classifier methodology , 1991, IEEE Trans. Syst. Man Cybern..

[30]  S. Dwivedi,et al.  Obesity May Be Bad: Compressed Convolutional Networks for Biomedical Image Segmentation , 2020 .

[31]  Mariette DiChristina,et al.  Promises and Perils. , 2015 .

[32]  I. Mahé,et al.  A Prospective Study , 2009 .