AIMC Topic: Retina

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Detection of smoking status from retinal images; a Convolutional Neural Network study.

Scientific reports May 9, 2019
Cardiovascular diseases are directly linked to smoking habits, which has both physiological and anatomical effects on the systemic and retinal circulations, and these changes can be detected with fundus photographs. Here, we aimed to 1- design a Conv...
Humans Diabetic Retinopathy Aged Adult Male Middle Aged Neural Networks, Computer Databases, Factual Female Smoking ROC Curve Area Under Curve Retina
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A pilot study of autonomous artificial intelligence-based diabetic retinopathy screening in Poland.

Acta ophthalmologica May 3, 2019
Poland Diagnosis, Computer-Assisted Pilot Projects Mass Screening Humans Retina Diabetic Retinopathy Physical Examination Artificial Intelligence Vision Screening
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Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a clinical validation study.

The Lancet. Digital health May 2, 2019
BACKGROUND: Radical measures are required to identify and reduce blindness due to diabetes to achieve the Sustainable Development Goals by 2030. Therefore, we evaluated the accuracy of an artificial intelligence (AI) model using deep learning in a po...
Artificial Intelligence Prospective Studies Female Adult Humans Diabetic Retinopathy Neural Networks, Computer Area Under Curve Retina Photography Zambia Sensitivity and Specificity Male Deep Learning Mass Screening
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Comparison between two programs for image analysis, machine learning and subsequent classification.

Tissue & cell Mar 27, 2019
In the early 1950s, flow cytometry was developed as the first method for automated quantitative cellular analysis. In the early 1990s, the first equipment for image cytometry (laser scanning cytometry, LSC) became commercially available. As flow cyto...
Machine Learning Retina Image Processing, Computer-Assisted Kidney Rats, Wistar Rats Software Animals Male
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A Novel Weakly Supervised Multitask Architecture for Retinal Lesions Segmentation on Fundus Images.

IEEE transactions on medical imaging Mar 20, 2019
Obtaining the complete segmentation map of retinal lesions is the first step toward an automated diagnosis tool for retinopathy that is interpretable in its decision-making. However, the limited availability of ground truth lesion detection maps at a...
Image Interpretation, Computer-Assisted Retinal Diseases Diagnostic Techniques, Ophthalmological ROC Curve Fundus Oculi Humans Retina Databases, Factual Supervised Machine Learning
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Scalable analysis of cell-type composition from single-cell transcriptomics using deep recurrent learning.

Nature methods Mar 18, 2019
Recent advances in large-scale single-cell RNA-seq enable fine-grained characterization of phenotypically distinct cellular states in heterogeneous tissues. We present scScope, a scalable deep-learning-based approach that can accurately and rapidly i...
Software Algorithms Phenotype Computer Simulation RNA Mice Brain Mapping Computational Biology Principal Component Analysis Sequence Analysis, RNA Retina Gene Expression Profiling Intestines Reproducibility of Results Transcriptome Inflammation Animals Deep Learning Databases, Genetic Leukocytes, Mononuclear Cluster Analysis Single-Cell Analysis
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CE-Net: Context Encoder Network for 2D Medical Image Segmentation.

IEEE transactions on medical imaging Mar 7, 2019
Medical image segmentation is an important step in medical image analysis. With the rapid development of a convolutional neural network in image processing, deep learning has been used for medical image segmentation, such as optic disc segmentation, ...
Lung Image Processing, Computer-Assisted Retina Retinal Vessels Humans Neural Networks, Computer Algorithms
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Retinal Image Synthesis and Semi-Supervised Learning for Glaucoma Assessment.

IEEE transactions on medical imaging Mar 7, 2019
Recent works show that generative adversarial networks (GANs) can be successfully applied to image synthesis and semi-supervised learning, where, given a small labeled database and a large unlabeled database, the goal is to train a powerful classifie...
Image Interpretation, Computer-Assisted Diagnostic Techniques, Ophthalmological Databases, Factual Retina Supervised Machine Learning Humans Glaucoma Algorithms
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Deep-learning based multiclass retinal fluid segmentation and detection in optical coherence tomography images using a fully convolutional neural network.

Medical image analysis Feb 22, 2019
As a non-invasive imaging modality, optical coherence tomography (OCT) can provide micrometer-resolution 3D images of retinal structures. These images can help reveal disease-related alterations below the surface of the retina, such as the presence o...
Image Processing, Computer-Assisted Tomography, Optical Coherence Retina Humans Deep Learning Imaging, Three-Dimensional
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Glaucoma Diagnosis with Machine Learning Based on Optical Coherence Tomography and Color Fundus Images.

Journal of healthcare engineering Feb 18, 2019
This study aimed to develop a machine learning-based algorithm for glaucoma diagnosis in patients with open-angle glaucoma, based on three-dimensional optical coherence tomography (OCT) data and color fundus images. In this study, 208 glaucomatous an...
Male Algorithms Tomography, Optical Coherence Humans Middle Aged Female Fundus Oculi Area Under Curve Glaucoma Aged Retina Adult Machine Learning Image Interpretation, Computer-Assisted
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