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 algorithms for detection of critical findings in head CT scans: a retrospective study
Background Non-contrast head CT scan is the current standard for initial imaging of patients
with head trauma or stroke symptoms. We aimed to develop and validate a set of deep …
with head trauma or stroke symptoms. We aimed to develop and validate a set of deep …
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 …
Convolutional neural network with shape prior applied to cardiac MRI segmentation
In this paper, we present a novel convolutional neural network architecture to segment
images from a series of short-axis cardiac magnetic resonance slices (CMRI). The proposed …
images from a series of short-axis cardiac magnetic resonance slices (CMRI). The proposed …
Full left ventricle quantification via deep multitask relationships learning
Cardiac left ventricle (LV) quantification is among the most clinically important tasks for
identification and diagnosis of cardiac disease. However, it is still a task of great challenge …
identification and diagnosis of cardiac disease. However, it is still a task of great challenge …
Development and validation of deep learning algorithms for detection of critical findings in head CT scans
Importance: Non-contrast head CT scan is the current standard for initial imaging of patients
with head trauma or stroke symptoms. Objective: To develop and validate a set of deep …
with head trauma or stroke symptoms. Objective: To develop and validate a set of deep …
Comparison of different convolutional neural network architectures for satellite image segmentation
V Khryashchev, L Ivanovsky, V Pavlov… - … 23rd conference of …, 2018 - ieeexplore.ieee.org
Convolutional neural networks for detection geo-objects on the satellite images from DSTL,
Landsat-8 and PlanetScope databases were analyzed. Three modification of convolutional …
Landsat-8 and PlanetScope databases were analyzed. Three modification of convolutional …
Fully automatic segmentation of the right ventricle via multi-task deep neural networks
Segmentation of ventricles from cardiac magnetic resonance (MR) images is a key step to
obtaining clinical parameters useful for prognosis of cardiac pathologies. To improve upon …
obtaining clinical parameters useful for prognosis of cardiac pathologies. To improve upon …
Vessel layer separation in x-ray angiograms with fully convolutional network
H Hao, H Ma, T van Walsum - Medical imaging 2018: Image …, 2018 - spiedigitallibrary.org
Percutaneous coronary intervention is a minimally-invasive procedure to treat coronary
artery disease. In such procedures, X-ray angiography, a real-time imaging technique, is …
artery disease. In such procedures, X-ray angiography, a real-time imaging technique, is …
Combining deep learning and shape priors for bi-ventricular segmentation of volumetric cardiac magnetic resonance images
In this paper, we combine a network-based method with image registration to develop a
shape-based bi-ventricular segmentation tool for short-axis cardiac magnetic resonance …
shape-based bi-ventricular segmentation tool for short-axis cardiac magnetic resonance …