Artificial intelligence in hypertension management: an ace up your sleeve
Arterial hypertension (AH) is a progressive issue that grows in importance with the increased
average age of the world population. The potential role of artificial intelligence (AI) in its …
average age of the world population. The potential role of artificial intelligence (AI) in its …
A comprehensive review and analysis of deep learning-based medical image adversarial attack and defense
Deep learning approaches have demonstrated great achievements in the field of computer-
aided medical image analysis, improving the precision of diagnosis across a range of …
aided medical image analysis, improving the precision of diagnosis across a range of …
Vulnerability in deep transfer learning models to adversarial fast gradient sign attack for covid-19 prediction from chest radiography images
The COVID-19 pandemic requires the rapid isolation of infected patients. Thus, high-
sensitivity radiology images could be a key technique to diagnose patients besides the …
sensitivity radiology images could be a key technique to diagnose patients besides the …
Adversarial attack and defense for medical image analysis: Methods and applications
Deep learning techniques have achieved superior performance in computer-aided medical
image analysis, yet they are still vulnerable to imperceptible adversarial attacks, resulting in …
image analysis, yet they are still vulnerable to imperceptible adversarial attacks, resulting in …
Computation of a probabilistic and anisotropic failure metric on the aortic wall using a machine learning-based surrogate model
Scalar-valued failure metrics are commonly used to assess the risk of aortic aneurysm
rupture and dissection, which occurs under hypertensive blood pressures brought on by …
rupture and dissection, which occurs under hypertensive blood pressures brought on by …
A regularization method to improve adversarial robustness of neural networks for ECG signal classification
With the advancement of machine leaning technologies, Deep Neural Networks (DNNs)
have been utilized for automated interpretation of Electrocardiogram (ECG) signals to …
have been utilized for automated interpretation of Electrocardiogram (ECG) signals to …
[HTML][HTML] Multi-fidelity surrogate modeling through hybrid machine learning for biomechanical and finite element analysis of soft tissues
SS Sajjadinia, B Carpentieri, D Shriram… - Computers in Biology …, 2022 - Elsevier
Biomechanical simulation enables medical researchers to study complex mechano-
biological conditions, although for soft tissue modeling, it may apply highly nonlinear multi …
biological conditions, although for soft tissue modeling, it may apply highly nonlinear multi …
A quantum-classical hybrid solution for deep anomaly detection
Machine learning (ML) has achieved remarkable success in a wide range of applications. In
recent ML research, deep anomaly detection (AD) has been a hot topic with the aim of …
recent ML research, deep anomaly detection (AD) has been a hot topic with the aim of …
Synergistic integration of deep neural networks and finite element method with applications of nonlinear large deformation biomechanics
Patient-specific finite element analysis (FEA) holds great promise in advancing the
prognosis of cardiovascular diseases by providing detailed biomechanical insights such as …
prognosis of cardiovascular diseases by providing detailed biomechanical insights such as …
Survey on Adversarial Attack and Defense for Medical Image Analysis: Methods and Challenges
Deep learning techniques have achieved superior performance in computer-aided medical
image analysis, yet they are still vulnerable to imperceptible adversarial attacks, resulting in …
image analysis, yet they are still vulnerable to imperceptible adversarial attacks, resulting in …