AIMC Topic:
Image Interpretation, Computer-Assisted

Clear Filters Showing 1781 to 1790 of 2747 articles

Patch-Based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentation.

IEEE transactions on medical imaging Feb 18, 2019
Glaucoma is a leading cause of irreversible blindness. Accurate segmentation of the optic disc (OD) and optic cup (OC) from fundus images is beneficial to glaucoma screening and diagnosis. Recently, convolutional neural networks demonstrate promising...
ROC Curve Fundus Oculi Image Interpretation, Computer-Assisted Optic Disk Diagnostic Techniques, Ophthalmological Glaucoma Databases, Factual Humans Neural Networks, Computer
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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...
Adult Male Middle Aged Image Interpretation, Computer-Assisted Fundus Oculi Algorithms Retina Humans Female Glaucoma Area Under Curve Aged Machine Learning Tomography, Optical Coherence
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Fully Convolutional Networks for Monocular Retinal Depth Estimation and Optic Disc-Cup Segmentation.

IEEE journal of biomedical and health informatics Feb 14, 2019
Glaucoma is a serious ocular disorder for which the screening and diagnosis are carried out by the examination of the optic nerve head (ONH). The color fundus image (CFI) is the most common modality used for ocular screening. In CFI, the central regi...
Humans Neural Networks, Computer Glaucoma Optic Disk Diagnostic Techniques, Ophthalmological ROC Curve Retina Image Interpretation, Computer-Assisted
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Random forest prediction of Alzheimer's disease using pairwise selection from time series data.

PloS one Feb 14, 2019
Time-dependent data collected in studies of Alzheimer's disease usually has missing and irregularly sampled data points. For this reason time series methods which assume regular sampling cannot be applied directly to the data without a pre-processing...
Female Alzheimer Disease Aged Middle Aged Image Interpretation, Computer-Assisted Aged, 80 and over Humans Pattern Recognition, Automated Disease Progression Machine Learning Neuroimaging Male Magnetic Resonance Imaging
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Iterative fully convolutional neural networks for automatic vertebra segmentation and identification.

Medical image analysis Feb 12, 2019
Precise segmentation and anatomical identification of the vertebrae provides the basis for automatic analysis of the spine, such as detection of vertebral compression fractures or other abnormalities. Most dedicated spine CT and MR scans as well as s...
Fractures, Compression Magnetic Resonance Imaging Image Interpretation, Computer-Assisted Spinal Fractures Neural Networks, Computer Tomography, X-Ray Computed Humans
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Quantifying lung ultrasound comets with a convolutional neural network: Initial clinical results.

Computers in biology and medicine Feb 7, 2019
Lung ultrasound comets are "comet-tail" artifacts appearing in lung ultrasound images. They are particularly useful in detecting several lung pathologies and may indicate the amount of extravascular lung water. However, the comets are not always well...
Blood Pressure Image Interpretation, Computer-Assisted Artifacts Extravascular Lung Water Male Female Middle Aged Lung Aged Ultrasonography Adult Humans Neural Networks, Computer
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Comparative assessment of CNN architectures for classification of breast FNAC images.

Tissue & cell Feb 5, 2019
Fine needle aspiration cytology (FNAC) entails using a narrow gauge (25-22 G) needle to collect a sample of a lesion for microscopic examination. It allows a minimally invasive, rapid diagnosis of tissue but does not preserve its histological archite...
Deep Learning Image Interpretation, Computer-Assisted Biopsy, Fine-Needle Humans Female Breast Neoplasms
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Attention gated networks: Learning to leverage salient regions in medical images.

Medical image analysis Feb 5, 2019
We propose a novel attention gate (AG) model for medical image analysis that automatically learns to focus on target structures of varying shapes and sizes. Models trained with AGs implicitly learn to suppress irrelevant regions in an input image whi...
Neural Networks, Computer Algorithms Tomography, X-Ray Computed Ultrasonography, Prenatal Imaging, Three-Dimensional Pregnancy Female Humans Image Interpretation, Computer-Assisted Datasets as Topic Radiography, Abdominal
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Radiomics-based machine learning methods for isocitrate dehydrogenase genotype prediction of diffuse gliomas.

Journal of cancer research and clinical oncology Feb 4, 2019
PURPOSE: Reliable and accurate predictive models are necessary to drive the success of radiomics. Our aim was to identify the optimal radiomics-based machine learning method for isocitrate dehydrogenase (IDH) genotype prediction in diffuse gliomas.
Humans Machine Learning Middle Aged Adolescent Genotype Female Young Adult Image Interpretation, Computer-Assisted Aged Glioma Adult Brain Neoplasms Isocitrate Dehydrogenase Aged, 80 and over Magnetic Resonance Imaging Male
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Intracranial Vessel Wall Segmentation Using Convolutional Neural Networks.

IEEE transactions on bio-medical engineering Feb 1, 2019
OBJECTIVE: To develop an automated vessel wall segmentation method using convolutional neural networks to facilitate the quantification on magnetic resonance (MR) vessel wall images of patients with intracranial atherosclerotic disease (ICAD).
Female Risk Factors Male Middle Aged Humans Neural Networks, Computer Retrospective Studies Automation Magnetic Resonance Angiography Middle Cerebral Artery Intracranial Arteriosclerosis Imaging, Three-Dimensional Contrast Media Image Interpretation, Computer-Assisted
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