AIMC Topic:
Image Interpretation, Computer-Assisted
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A speckle-tracking strain-based artificial neural network model to differentiate cardiomyopathy type.
Scandinavian cardiovascular journal : SCJ
Oct 18, 2019
In heart failure, invasive angiography is often employed to differentiate ischaemic from non-ischaemic cardiomyopathy. We aim to examine the predictive value of echocardiographic strain features alone and in combination with other features to differ...
Ventricular Function, Left
Stroke Volume
Echocardiography
Middle Aged
Humans
Diagnosis, Computer-Assisted
Diagnosis, Differential
Predictive Value of Tests
Heart Failure
Female
Reproducibility of Results
Neural Networks, Computer
Aged
Image Interpretation, Computer-Assisted
Prognosis
Cardiomyopathies
Retrospective Studies
Male
Fully automatic segmentation of type B aortic dissection from CTA images enabled by deep learning.
European journal of radiology
Oct 17, 2019
PURPOSE: This study sought to establish a robust and fully automated Type B aortic dissection (TBAD) segmentation method by leveraging the emerging deep learning techniques.
A Super-Learner Model for Tumor Motion Prediction and Management in Radiation Therapy: Development and Feasibility Evaluation.
Scientific reports
Oct 16, 2019
In cancer radiation therapy, large tumor motion due to respiration can lead to uncertainties in tumor target delineation and treatment delivery, thus making active motion management an essential step in thoracic and abdominal tumor treatment. In curr...
Hippocampal segmentation for brains with extensive atrophy using three-dimensional convolutional neural networks.
Human brain mapping
Oct 14, 2019
Hippocampal volumetry is a critical biomarker of aging and dementia, and it is widely used as a predictor of cognitive performance; however, automated hippocampal segmentation methods are limited because the algorithms are (a) not publicly available,...
Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics.
NeuroImage
Oct 11, 2019
There is significant interest in the development and application of deep neural networks (DNNs) to neuroimaging data. A growing literature suggests that DNNs outperform their classical counterparts in a variety of neuroimaging applications, yet there...
Deep Learning in Quantitative PET Myocardial Perfusion Imaging: A Study on Cardiovascular Event Prediction.
JACC. Cardiovascular imaging
Oct 11, 2019
Myocardial Perfusion Imaging
Models, Cardiovascular
Predictive Value of Tests
Positron Emission Tomography Computed Tomography
Coronary Artery Disease
Aged
Image Interpretation, Computer-Assisted
Prognosis
Male
Middle Aged
Female
Risk Assessment
Humans
Disease Progression
Reproducibility of Results
Diagnosis, Computer-Assisted
Risk Factors
Deep Learning
Time Factors
The quantization error in a Self-Organizing Map as a contrast and colour specific indicator of single-pixel change in large random patterns.
Neural networks : the official journal of the International Neural Network Society
Oct 11, 2019
The quantization error in a fixed-size Self-Organizing Map (SOM) with unsupervised winner-take-all learning has previously been used successfully to detect, in minimal computation time, highly meaningful changes across images in medical time series a...
Multi-task recurrent convolutional network with correlation loss for surgical video analysis.
Medical image analysis
Oct 10, 2019
Surgical tool presence detection and surgical phase recognition are two fundamental yet challenging tasks in surgical video analysis as well as very essential components in various applications in modern operating rooms. While these two analysis task...
Joint optic disc and cup segmentation using semi-supervised conditional GANs.
Computers in biology and medicine
Oct 10, 2019
Glaucoma is a chronic and widespread eye disease threatening humans' irreversible vision loss. The cup-to-disc ratio (CDR), one of the most important measurements used for glaucoma screening and diagnosis, requires accurate segmentation of optic disc...
Fetal Congenital Heart Disease Echocardiogram Screening Based on DGACNN: Adversarial One-Class Classification Combined with Video Transfer Learning.
IEEE transactions on medical imaging
Oct 7, 2019
Fetal congenital heart disease (FHD) is a common and serious congenital malformation in children. In Asia, FHD birth defect rates have reached as high as 9.3%. For the early detection of birth defects and mortality, echocardiography remains the most ...
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