A survey on deep learning in medical image analysis
Deep learning algorithms, in particular convolutional networks, have rapidly become a
methodology of choice for analyzing medical images. This paper reviews the major deep …
methodology of choice for analyzing medical images. This paper reviews the major deep …
Deep learning in cardiology
P Bizopoulos, D Koutsouris - IEEE reviews in biomedical …, 2018 - ieeexplore.ieee.org
The medical field is creating large amount of data that physicians are unable to decipher
and use efficiently. Moreover, rule-based expert systems are inefficient in solving …
and use efficiently. Moreover, rule-based expert systems are inefficient in solving …
Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved?
Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac
magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish …
magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish …
Fully convolutional multi-scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers
Deep fully convolutional neural network (FCN) based architectures have shown great
potential in medical image segmentation. However, such architectures usually have millions …
potential in medical image segmentation. However, such architectures usually have millions …
Automated diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging using deep learning models: A review
In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of
mortality globally. At early stages, CVDs appear with minor symptoms and progressively get …
mortality globally. At early stages, CVDs appear with minor symptoms and progressively get …
[HTML][HTML] Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning
Good quality of medical images is a prerequisite for the success of subsequent image
analysis pipelines. Quality assessment of medical images is therefore an essential activity …
analysis pipelines. Quality assessment of medical images is therefore an essential activity …
Deep convolutional neural network in medical image processing
Researchers have started constructing systems that could automatically analyze the medical
images. In the initial phase (starting from 1970 to 1990), image processing was carried out …
images. In the initial phase (starting from 1970 to 1990), image processing was carried out …
Automated quality control in image segmentation: application to the UK Biobank cardiovascular magnetic resonance imaging study
Background The trend towards large-scale studies including population imaging poses new
challenges in terms of quality control (QC). This is a particular issue when automatic …
challenges in terms of quality control (QC). This is a particular issue when automatic …
Evaluation of transfer learning in deep convolutional neural network models for cardiac short axis slice classification
In computer-aided analysis of cardiac MRI data, segmentations of the left ventricle (LV) and
myocardium are performed to quantify LV ejection fraction and LV mass, and they are …
myocardium are performed to quantify LV ejection fraction and LV mass, and they are …
Quantitative CMR population imaging on 20,000 subjects of the UK Biobank imaging study: LV/RV quantification pipeline and its evaluation
Population imaging studies generate data for develo** and implementing personalised
health strategies to prevent, or more effectively treat disease. Large prospective …
health strategies to prevent, or more effectively treat disease. Large prospective …