The fully convolutional transformer for medical image segmentation
We propose a novel transformer model, capable of segmenting medical images of varying
modalities. Challenges posed by the fine-grained nature of medical image analysis mean …
modalities. Challenges posed by the fine-grained nature of medical image analysis mean …
Auto-MyIn: Automatic diagnosis of myocardial infarction via multiple GLCMs, CNNs, and SVMs
O Attallah, DA Ragab - Biomedical Signal Processing and Control, 2023 - Elsevier
This paper proposes an automated diagnostic tool namely, Auto-MyIn, for diagnosing
myocardial infarction (MI) using multiple convolutional neural networks (CNN). Rather than …
myocardial infarction (MI) using multiple convolutional neural networks (CNN). Rather than …
Deep learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge
A key factor for assessing the state of the heart after myocardial infarction (MI) is to measure
whether the myocardium segment is viable after reperfusion or revascularization therapy …
whether the myocardium segment is viable after reperfusion or revascularization therapy …
[HTML][HTML] Attri-VAE: Attribute-based interpretable representations of medical images with variational autoencoders
Deep learning (DL) methods where interpretability is intrinsically considered as part of the
model are required to better understand the relationship of clinical and imaging-based …
model are required to better understand the relationship of clinical and imaging-based …
Automatic classification of patients with myocardial infarction or myocarditis based only on clinical data: A quick response
Background In acute cardiovascular disease management, the delay between the
admission in a hospital emergency department and the assessment of the disease from a …
admission in a hospital emergency department and the assessment of the disease from a …
An improved 3D deep learning-based segmentation of left ventricular myocardial diseases from delayed-enhancement MRI with inclusion and classification prior …
Accurate segmentation of the myocardial scar may supply relevant advancements in
predicting and controlling deadly ventricular arrhythmias in subjects with cardiovascular …
predicting and controlling deadly ventricular arrhythmias in subjects with cardiovascular …
Leveraging uncertainty estimates to improve segmentation performance in cardiac MR
In medical image segmentation, several studies have used Bayesian neural networks to
segment and quantify the uncertainty of the images. These studies show that there might be …
segment and quantify the uncertainty of the images. These studies show that there might be …
Enhancing Myocardial Disease Prediction with DOC-NET+ Architecture: A Custom Data Analysis Approach for the EMIDEC Challenge
M Dali, R Kachouri, N Benameur, Y Arous… - Procedia Computer …, 2024 - Elsevier
Public health experts are deeply concerned about cardiovascular diseases, including
numerous heart-related ailments that can prove fatal. Distinguishing between Myocarditis …
numerous heart-related ailments that can prove fatal. Distinguishing between Myocarditis …
Deep learning for automatic detection and quantification of the disease areas of the myocardium from DE-MRI after myocardial infarction
Z Chen - 2021 - theses.hal.science
Myocardial Infarction (MI) has become one of the most common cardiovascular diseases.
The MI occurs when the blood flow decreases or stops to a part of the heart which can cause …
The MI occurs when the blood flow decreases or stops to a part of the heart which can cause …
Deep learning architectures for automatic detection of viable myocardiac segments
K Brahim - 2021 - theses.hal.science
Thesis abstract: Deep learning architectures for automatic detection of viable myocardiac
segmentsAccurate myocardial segmentation in LGE-MRI is an important purpose for …
segmentsAccurate myocardial segmentation in LGE-MRI is an important purpose for …