179 citations · 297 across the 11 of their papers we have counts for
11 papers
NeCA: 3D Coronary Artery Tree Reconstruction from Two 2D Projections via Neural Implicit Representation
Yiying Wang, Abhirup Banerjee, Vicente Grau
Cardiovascular diseases (CVDs) are the most common health threats worldwide. 2D X-ray invasive coronary angiography (ICA) remains the most widely adopted imaging modality for CVD a…
DeepCA: Deep Learning-based 3D Coronary Artery Tree Reconstruction from Two 2D Non-simultaneous X-ray Angiography Projections
Yiying Wang, Abhirup Banerjee, Robin P. Choudhury +1
Cardiovascular diseases (CVDs) are the most common cause of death worldwide. Invasive x-ray coronary angiography (ICA) is one of the most important imaging modalities for the diagn…
Large Language Model-informed ECG Dual Attention Network for Heart Failure Risk Prediction
Chen Chen, Lei Li, Marcel Beetz +3
Heart failure (HF) poses a significant public health challenge, with a rising global mortality rate. Early detection and prevention of HF could significantly reduce its impact. We…
Anatomical basis of sex differences in the electrocardiogram identified by three-dimensional torso-heart imaging reconstruction pipeline
Hannah J. Smith, Blanca Rodriguez, Yuling Sang +4
The electrocardiogram (ECG) is used for diagnosis and risk stratification in myocardial infarction (MI). Women have a higher incidence of missed MI diagnosis and complications foll…
Multi-objective point cloud autoencoders for explainable myocardial infarction prediction
Marcel Beetz, Abhirup Banerjee, Vicente Grau
Myocardial infarction (MI) is one of the most common causes of death in the world. Image-based biomarkers commonly used in the clinic, such as ejection fraction, fail to capture mo…
Modeling 3D cardiac contraction and relaxation with point cloud deformation networks
Marcel Beetz, Abhirup Banerjee, Vicente Grau
Global single-valued biomarkers of cardiac function typically used in clinical practice, such as ejection fraction, provide limited insight on the true 3D cardiac deformation proce…
Multi-class point cloud completion networks for 3D cardiac anatomy reconstruction from cine magnetic resonance images
Marcel Beetz, Abhirup Banerjee, Julius Ossenberg-Engels +1
Cine magnetic resonance imaging (MRI) is the current gold standard for the assessment of cardiac anatomy and function. However, it typically only acquires a set of two-dimensional…
3D Shape-Based Myocardial Infarction Prediction Using Point Cloud Classification Networks
Marcel Beetz, Yilong Yang, Abhirup Banerjee +2
Myocardial infarction (MI) is one of the most prevalent cardiovascular diseases with associated clinical decision-making typically based on single-valued imaging biomarkers. Howeve…
Deep Computational Model for the Inference of Ventricular Activation Properties
Lei Li, Julia Camps, Abhirup Banerjee +3
Patient-specific cardiac computational models are essential for the efficient realization of precision medicine and in-silico clinical trials using digital twins. Cardiac digital t…
DenResCov-19: A deep transfer learning network for robust automatic classification of COVID-19, pneumonia, and tuberculosis from X-rays
Michail Mamalakis, Andrew J. Swift, Bart Vorselaars +6
The global pandemic of COVID-19 is continuing to have a significant effect on the well-being of global population, increasing the demand for rapid testing, diagnosis, and treatment…
Use of Machine Learning and Artificial Intelligence to predict SARS-CoV-2 infection from Full Blood Counts in a population
Abhirup Banerjee, Surajit Ray, Bart Vorselaars +5
Since December 2019 the novel coronavirus SARS-CoV-2 has been identified as the cause of the pandemic COVID-19. Early symptoms overlap with other common conditions such as common c…