Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs
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Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework.Improving Consensus Scoring of Crowdsourced Data Using the Rasch Model: Development and Refinement of a Diagnostic InstrumentHigh-Definition Medicine.Applying deep neural networks to unstructured text notes in electronic medical records for phenotyping youth depression.Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks.Artificial Intelligence Methodologies and Their Application to Diabetes.Large-scale image region documentation for fully automated image biomarker algorithm development and evaluation.Deep Convolutional Neural Networks for Endotracheal Tube Position and X-ray Image Classification: Challenges and Opportunities.Deep learning for healthcare: review, opportunities and challenges.Point-of-care mobile digital microscopy and deep learning for the detection of soil-transmitted helminths and Schistosoma haematobium.Reaching the Unreachable: Novel Approaches to Telemedicine Screening of Underserved Populations for Vitreoretinal Disease.Retinal vascular geometry and 6 year incidence and progression of diabetic retinopathy.Does Machine Learning Automate Moral Hazard and Error?Accuracy of deep learning, a machine-learning technology, using ultra-wide-field fundus ophthalmoscopy for detecting rhegmatogenous retinal detachment.Automatic diagnosis of imbalanced ophthalmic images using a cost-sensitive deep convolutional neural network.Clinical judgement in the era of big data and predictive analytics.Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes.White blood cell differential count of maturation stages in bone marrow smear using dual-stage convolutional neural networks.The influence of big (clinical) data and genomics on precision medicine and drug development.Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning.Rapid Intraoperative Diagnosis of Pediatric Brain Tumors Using Stimulated Raman Histology.Machine Learning Approaches in Cardiovascular Imaging.Crowdsourcing and Automated Retinal Image Analysis for Diabetic Retinopathy.Precision Medicine for Heart Failure with Preserved Ejection Fraction: An Overview.Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer.Application of Convolutional Neural Networks in the Diagnosis of Helicobacter pylori Infection Based on Endoscopic Images.Leveraging uncertainty information from deep neural networks for disease detection.Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database.Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning.Recent advances in the management and understanding of diabetic retinopathy.Automatic Detection of Acromegaly From Facial Photographs Using Machine Learning Methods.Novel Image-Based Analysis for Reduction of Clinician-Dependent Variability in Measurement of the Corneal Ulcer Size.Implementation of Enterprise Imaging Strategy at a Chinese Tertiary Hospital.Residual Convolutional Neural Network for Determination of IDH Status in Low- and High-grade Gliomas from MR Imaging.Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images.Epileptic Seizure Prediction Using Big Data and Deep Learning: Toward a Mobile System.Classification of images based on small local features: a case applied to microaneurysms in fundus retina images.A novel microaneurysms detection approach based on convolutional neural networks with reinforcement sample learning algorithm.Artificial Intelligence, Physiological Genomics, and Precision Medicine.Sensitivity and specificity of automated analysis of single-field non-mydriatic fundus photographs by Bosch DR Algorithm-Comparison with mydriatic fundus photography (ETDRS) for screening in undiagnosed diabetic retinopathy.
P2860
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P2860
Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs
description
2016 nî lūn-bûn
@nan
2016 թուականի Նոյեմբերին հրատարակուած գիտական յօդուած
@hyw
2016 թվականի նոյեմբերին հրատարակված գիտական հոդված
@hy
2016年の論文
@ja
2016年論文
@yue
2016年論文
@zh-hant
2016年論文
@zh-hk
2016年論文
@zh-mo
2016年論文
@zh-tw
2016年论文
@wuu
name
Development and Validation of ...... in Retinal Fundus Photographs
@ast
Development and Validation of ...... in Retinal Fundus Photographs
@en
Development and Validation of ...... in Retinal Fundus Photographs
@nl
type
label
Development and Validation of ...... in Retinal Fundus Photographs
@ast
Development and Validation of ...... in Retinal Fundus Photographs
@en
Development and Validation of ...... in Retinal Fundus Photographs
@nl
prefLabel
Development and Validation of ...... in Retinal Fundus Photographs
@ast
Development and Validation of ...... in Retinal Fundus Photographs
@en
Development and Validation of ...... in Retinal Fundus Photographs
@nl
P2093
P356
P1476
Development and Validation of ...... in Retinal Fundus Photographs
@en
P2093
Arunachalam Narayanaswamy
Dale R. Webster
Jessica L. Mega
Jorge Cuadros
Kasumi Widner
Marc Coram
Martin C. Stumpe
Philip C. Nelson
P304
P356
10.1001/JAMA.2016.17216
P407
P577
2016-11-29T00:00:00Z