AIMC Topic: Retina
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Deep Learning Based Sub-Retinal Fluid Segmentation in Central Serous Chorioretinopathy Optical Coherence Tomography Scans.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2019
Development of an automated sub-retinal fluid segmentation technique from optical coherence tomography (OCT) scans is faced with challenges such as noise and motion artifacts present in OCT images, variation in size, shape and location of fluid pocke...
Recalibrating Fully Convolutional Networks With Spatial and Channel "Squeeze and Excitation" Blocks.
IEEE transactions on medical imaging
Feb 1, 2019
In a wide range of semantic segmentation tasks, fully convolutional neural networks (F-CNNs) have been successfully leveraged to achieve the state-of-the-art performance. Architectural innovations of F-CNNs have mainly been on improving spatial encod...
Applications of Deep Learning and Artificial Intelligence in Retina.
International ophthalmology clinics
Jan 1, 2019
[Deep learning to support therapy decisions for intravitreal injections].
Der Ophthalmologe : Zeitschrift der Deutschen Ophthalmologischen Gesellschaft
Sep 1, 2018
Significant progress has been made in artificial intelligence and computer vision research in recent years. Machine learning methods excel in a wide variety of tasks where sufficient data are available. We describe the application of a deep convoluti...
Multi-Cell Multi-Task Convolutional Neural Networks for Diabetic Retinopathy Grading.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2018
Diabetic Retinopathy (DR) is a non-negligible eye disease among patients with Diabetes Mellitus, and automatic retinal image analysis algorithm for the DR screening is in high demand. Considering the resolution of retinal image is very high, where sm...
Deep Learning for Predicting Refractive Error From Retinal Fundus Images.
Investigative ophthalmology & visual science
Jun 1, 2018
PURPOSE: We evaluate how deep learning can be applied to extract novel information such as refractive error from retinal fundus imaging.
Eyeing cardiovascular risk factors.
Nature biomedical engineering
Mar 1, 2018
All eyes are on AI.
Nature biomedical engineering
Mar 1, 2018
Deep learning sharpens views of cells and genes.
Nature
Jan 4, 2018
Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes.
JAMA
Dec 12, 2017
IMPORTANCE: A deep learning system (DLS) is a machine learning technology with potential for screening diabetic retinopathy and related eye diseases.
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