Machine Learning Techniques in Clinical Vision Sciences.

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Machine learning for epigenetics and future medical applications.Classification of optic disc shape in glaucoma using machine learning based on quantified ocular parameters.Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database.

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P2860

Machine Learning Techniques in Clinical Vision Sciences.

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Machine Learning Techniques in Clinical Vision Sciences.

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Machine Learning Techniques in Clinical Vision Sciences.

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Machine Learning Techniques in Clinical Vision Sciences

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P2093

Miguel Caixinha

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Sandrina Nunes

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P2860

Prediction of Coronary Heart Disease Using Risk Factor Categories

Classification of Farnsworth-Munsell 100-hue test results in the early treatment diabetic retinopathy study

Missing data: our view of the state of the art

An introduction to ROC analysis

Three-dimensional spectral-domain optical coherence tomography data analysis for glaucoma detection

Machine learning for medical diagnosis: history, state of the art and perspective

Learning from data: recognizing glaucomatous defect patterns and detecting progression from visual field measurements

Analysis of GDx-VCC polarimetry data by Wavelet-Fourier analysis across glaucoma stages.

A nonparametric test for observational non-normally distributed ophthalmic data with eye-specific exposures and outcomes.

Fourier analysis of optical coherence tomography and scanning laser polarimetry retinal nerve fiber layer measurements in the diagnosis of glaucoma.

P304

1-15

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scholarly article

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10.1080/02713683.2016.1175019

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Sandrina Nunes

Miguel Caixinha

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2016-06-30T00:00:00Z

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27362387

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machine learning

Machine Learning Techniques in Clinical Vision Sciences.

about

P2860

P2860

Machine Learning Techniques in Clinical Vision Sciences.

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