Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks.
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Photoreceptor-Based Biomarkers in AOSLO Retinal Imaging.Deblurring adaptive optics retinal images using deep convolutional neural networks.Retinal Lesion Detection With Deep Learning Using Image Patches.Use of focus measure operators for characterization of flood illumination adaptive optics ophthalmoscopy image quality.Automatic Cone Photoreceptor Localisation in Healthy and Stargardt Afflicted Retinas Using Deep Learning.The Reliability of Cone Density Measurements in the Presence of Rods.Effect of patch size and network architecture on a convolutional neural network approach for automatic segmentation of OCT retinal layers.Cone Photoreceptor Cell Segmentation and Diameter Measurement on Adaptive Optics Images Using Circularly Constrained Active Contour ModelDeep learning based detection of cone photoreceptors with multimodal adaptive optics scanning light ophthalmoscope images of achromatopsiaResidual Cone Structure in Patients With X-Linked Cone Opsin MutationsAutomated identification of cone photoreceptors in adaptive optics optical coherence tomography images using transfer learning
P2860
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P2860
Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks.
description
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name
Open source software for autom ...... convolutional neural networks.
@en
type
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Open source software for autom ...... convolutional neural networks.
@en
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Open source software for autom ...... convolutional neural networks.
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P2093
P2860
P50
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Open source software for autom ...... convolutional neural networks
@en
P2093
David Cunefare
Joseph Carroll
Robert F Cooper
P2860
P2888
P356
10.1038/S41598-017-07103-0
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P577
2017-07-26T00:00:00Z
P6179
1090836438