[HTML][HTML] Machine learning approaches for myocardial motion and deformation analysis

N Duchateau, AP King, M De Craene - Frontiers in cardiovascular …, 2020 - frontiersin.org
Information about myocardial motion and deformation is key to differentiate normal and
abnormal conditions. With the advent of approaches relying on data rather than pre …

Deep learning for heart disease detection through cardiac sounds

L Brunese, F Martinelli, F Mercaldo… - Procedia Computer …, 2020 - Elsevier
Most of death causes are related to cardiovascular disease. In fact, there are several
anomalies afflicting the heart beat, for instance heart murmur or artefact. We propose a …

The relevance sample-feature machine: A sparse Bayesian learning approach to joint feature-sample selection

Y Mohsenzadeh, H Sheikhzadeh… - IEEE transactions on …, 2013 - ieeexplore.ieee.org
This paper introduces a novel sparse Bayesian machine-learning algorithm for embedded
feature selection in classification tasks. Our proposed algorithm, called the relevance …

CNN as model observer in a liver lesion detection task for x‐ray computed tomography: A phantom study

FK Kopp, M Catalano, D Pfeiffer, AA Fingerle… - Medical …, 2018 - Wiley Online Library
Purpose The purpose of this study was the evaluation of anthropomorphic model observers
trained with neural networks for the prediction of a human observer's performance. Methods …

Evaluation of the channelized Hotelling observer with an internal-noise model in a train-test paradigm for cardiac SPECT defect detection

JG Brankov - Physics in Medicine & Biology, 2013 - iopscience.iop.org
The channelized Hotelling observer (CHO) has become a widely used approach for
evaluating medical image quality, acting as a surrogate for human observers in early-stage …

Evaluation of CNN as anthropomorphic model observer

F Massanes, JG Brankov - Medical Imaging 2017: Image …, 2017 - spiedigitallibrary.org
Model observers (MO) are widely used in medical imaging to act as surrogates of human
observers in task-based image quality evaluation, frequently towards optimization of …

Automated estimation of image quality for coronary computed tomographic angiography using machine learning

R Nakanishi, S Sankaran, L Grady, J Malpeso… - European …, 2018 - Springer
Objectives Our goal was to evaluate the efficacy of a fully automated method for assessing
the image quality (IQ) of coronary computed tomography angiography (CCTA). Methods The …

Numerical surrogates for human observers in myocardial motion evaluation from SPECT images

T Marin, MM Kalayeh, FM Parages… - IEEE transactions on …, 2013 - ieeexplore.ieee.org
In medical imaging, the gold standard for image-quality assessment is a task-based
approach in which one evaluates human observer performance for a given diagnostic task …

Evaluation of a machine learning based model observer for x-ray CT

FK Kopp, M Catalano, D Pfeiffer… - Medical Imaging …, 2018 - spiedigitallibrary.org
In the medical imaging domain, image quality assessment is usually carried out by human
observers (HuO) performing a clinical task in reader studies. To overcome time-consuming …

Apprentissage statistique des interactions entre forme et déformation cardiaques

M Di Folco - 2021 - theses.hal.science
En routine clinique, les méthodes d'imagerie permettent d'extraire des indices caractérisant
la fonction cardiaque et d'établir un diagnostic. Dans le cas de l'insuffisance cardiaque, un …