AIMC Journal:
Medical image analysis
Showing 591 to 600 of 699 articles
Motion artifact recognition and quantification in coronary CT angiography using convolutional neural networks.
Medical image analysis
Nov 15, 2018
Excellent image quality is a primary prerequisite for diagnostic non-invasive coronary CT angiography. Artifacts due to cardiac motion may interfere with detection and diagnosis of coronary artery disease and render subsequent treatment decisions mor...
A graph-based lesion characterization and deep embedding approach for improved computer-aided diagnosis of nonmass breast MRI lesions.
Medical image analysis
Nov 2, 2018
Nonmass-like enhancements are a common but diagnostically challenging finding in breast MRI. Nonmass-like lesions can be described as clusters of spatially and temporally inter-connected regions of enhancements, so they can be modeled as networks and...
Automatic brain labeling via multi-atlas guided fully convolutional networks.
Medical image analysis
Nov 1, 2018
Multi-atlas-based methods are commonly used for MR brain image labeling, which alleviates the burdening and time-consuming task of manual labeling in neuroimaging analysis studies. Traditionally, multi-atlas-based methods first register multiple atla...
A collaborative computer aided diagnosis (C-CAD) system with eye-tracking, sparse attentional model, and deep learning.
Medical image analysis
Oct 28, 2018
Computer aided diagnosis (CAD) tools help radiologists to reduce diagnostic errors such as missing tumors and misdiagnosis. Vision researchers have been analyzing behaviors of radiologists during screening to understand how and why they miss tumors o...
3D regression neural network for the quantification of enlarged perivascular spaces in brain MRI.
Medical image analysis
Oct 26, 2018
Enlarged perivascular spaces (EPVS) in the brain are an emerging imaging marker for cerebral small vessel disease, and have been shown to be related to increased risk of various neurological diseases, including stroke and dementia. Automated quantifi...
Coronary artery centerline extraction in cardiac CT angiography using a CNN-based orientation classifier.
Medical image analysis
Oct 22, 2018
Coronary artery centerline extraction in cardiac CT angiography (CCTA) images is a prerequisite for evaluation of stenoses and atherosclerotic plaque. In this work, we propose an algorithm that extracts coronary artery centerlines in CCTA using a con...
Fully convolutional multi-scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers.
Medical image analysis
Oct 19, 2018
Deep fully convolutional neural network (FCN) based architectures have shown great potential in medical image segmentation. However, such architectures usually have millions of parameters and inadequate number of training samples leading to over-fitt...
Improvement of fully automated airway segmentation on volumetric computed tomographic images using a 2.5 dimensional convolutional neural net.
Medical image analysis
Oct 19, 2018
We propose a novel airway segmentation method in volumetric chest computed tomography (CT) and evaluate its performance on multiple datasets. The segmentation is performed voxel-by-voxel by a 2.5D convolutional neural net (2.5D CNN) trained in a supe...
Iterative multi-path tracking for video and volume segmentation with sparse point supervision.
Medical image analysis
Aug 29, 2018
Recent machine learning strategies for segmentation tasks have shown great ability when trained on large pixel-wise annotated image datasets. It remains a major challenge however to aggregate such datasets, as the time and monetary cost associated wi...
Building medical image classifiers with very limited data using segmentation networks.
Medical image analysis
Aug 4, 2018
Deep learning has shown promising results in medical image analysis, however, the lack of very large annotated datasets confines its full potential. Although transfer learning with ImageNet pre-trained classification models can alleviate the problem,...
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