AIMC Topic: Image Interpretation, Computer-Assisted

Clear Filters Showing 2401 to 2410 of 3033 articles

A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images.

IEEE transactions on bio-medical engineering Feb 26, 2016
GOAL: In this work, we present an extensive description and evaluation of our method for blood vessel segmentation in fundus images based on a discriminatively trained fully connected conditional random field model.
Fluorescein Angiography Humans Ophthalmoscopy Machine Learning Photography Retinal Artery Sensitivity and Specificity Models, Cardiovascular Models, Statistical Computer Simulation Reproducibility of Results Image Interpretation, Computer-Assisted Pattern Recognition, Automated Retinal Diseases
View on PubMed DOI

Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring.

IEEE transactions on medical imaging Feb 18, 2016
Mammographic risk scoring has commonly been automated by extracting a set of handcrafted features from mammograms, and relating the responses directly or indirectly to breast cancer risk. We present a method that learns a feature hierarchy from unlab...
Humans Female Adult Breast Neoplasms Risk Factors Aged Middle Aged Unsupervised Machine Learning Mammography Breast Breast Density Image Interpretation, Computer-Assisted
View on PubMed DOI

Evaluation of Underlying Lymphocytic Thyroiditis With Histogram Analysis Using Grayscale Ultrasound Images.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine Feb 17, 2016
OBJECTIVES: The purpose of this study was to evaluate diagnostic performance of histogram analysis using grayscale ultrasound (US) images in the diagnosis of lymphocytic thyroiditis.
Ultrasonography Data Interpretation, Statistical Image Interpretation, Computer-Assisted Observer Variation Male Middle Aged Adolescent Image Enhancement Thyroiditis, Autoimmune Female Reproducibility of Results Aged Adult Sensitivity and Specificity Aged, 80 and over Young Adult Humans Machine Learning Algorithms
View on PubMed DOI

Computer-Aided Endoscopic Diagnosis Without Human-Specific Labeling.

IEEE transactions on bio-medical engineering Feb 15, 2016
GOAL: Most state-of-the-art computer-aided endoscopic diagnosis methods require pixelwise labeled data to train various supervised machine learning models. However, it is a tedious and time-consuming work to collect sufficient precisely labeled image...
Image Interpretation, Computer-Assisted Databases, Factual Humans Endoscopy Machine Learning Algorithms Models, Theoretical
View on PubMed DOI

AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images.

IEEE transactions on medical imaging Feb 11, 2016
The lack of publicly available ground-truth data has been identified as the major challenge for transferring recent developments in deep learning to the biomedical imaging domain. Though crowdsourcing has enabled annotation of large scale databases f...
Mitosis Crowdsourcing Video Games Neural Networks, Computer Humans Machine Learning Image Interpretation, Computer-Assisted Female Breast Neoplasms Histocytochemistry Internet
View on PubMed DOI

Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation.

IEEE transactions on medical imaging Feb 11, 2016
We propose a novel segmentation approach based on deep 3D convolutional encoder networks with shortcut connections and apply it to the segmentation of multiple sclerosis (MS) lesions in magnetic resonance images. Our model is a neural network that co...
Neural Networks, Computer Machine Learning Algorithms Brain Humans Image Interpretation, Computer-Assisted Imaging, Three-Dimensional Magnetic Resonance Imaging Multiple Sclerosis
View on PubMed DOI

Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning.

IEEE transactions on medical imaging Feb 11, 2016
Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning data-driven, highly representative, hierarchical image fea...
Reproducibility of Results Diagnosis, Computer-Assisted Lymph Nodes Lung Diseases, Interstitial Image Interpretation, Computer-Assisted Humans Neural Networks, Computer Databases, Factual
View on PubMed DOI

Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks.

IEEE transactions on medical imaging Feb 11, 2016
Cerebral microbleeds (CMBs) are small haemorrhages nearby blood vessels. They have been recognized as important diagnostic biomarkers for many cerebrovascular diseases and cognitive dysfunctions. In current clinical routine, CMBs are manually labelle...
Imaging, Three-Dimensional Magnetic Resonance Imaging Cerebral Hemorrhage Image Interpretation, Computer-Assisted Databases, Factual Biomarkers Brain Humans Neural Networks, Computer
View on PubMed DOI

Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images.

IEEE transactions on medical imaging Feb 8, 2016
Convolutional neural networks (CNNs) are deep learning network architectures that have pushed forward the state-of-the-art in a range of computer vision applications and are increasingly popular in medical image analysis. However, training of CNNs is...
Databases, Factual Humans Neural Networks, Computer Fundus Oculi Machine Learning Image Interpretation, Computer-Assisted Retinal Hemorrhage
View on PubMed DOI

A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI.

Medical image analysis Feb 6, 2016
Segmentation of the left ventricle (LV) from cardiac magnetic resonance imaging (MRI) datasets is an essential step for calculation of clinical indices such as ventricular volume and ejection fraction. In this work, we employ deep learning algorithms...
Magnetic Resonance Imaging, Cine Pattern Recognition, Automated Image Enhancement Models, Cardiovascular Subtraction Technique Heart Ventricles Ventricular Dysfunction, Left Algorithms Image Interpretation, Computer-Assisted Humans Reproducibility of Results Computer Simulation Machine Learning Sensitivity and Specificity
View on PubMed DOI
Popular Topics
Recent Journals