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Found 10 papers, 1 papers with code
Large Language Model-informed ECG Dual Attention Network for Heart Failure Risk Prediction
We present a novel, lightweight dual-attention ECG network designed to capture complex ECG features essential for early HF risk prediction, despite the notable imbalance between low and high-risk groups.
Anatomical basis of sex differences in human post-myocardial infarction ECG phenotypes identified by novel automated torso-cardiac 3D reconstruction
Smaller ventricles in females explain ~50% of shorter QRS durations than in males, and contribute to lower STJ amplitudes in females (also due to more superior and posterior position).
Multi-objective point cloud autoencoders for explainable myocardial infarction prediction
In this work, we present the multi-objective point cloud autoencoder as a novel geometric deep learning approach for explainable infarction prediction, based on multi-class 3D point cloud representations of cardiac anatomy and function.
Modeling 3D cardiac contraction and relaxation with point cloud deformation networks
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 process and hence, limit the understanding of both healthy and pathological cardiac mechanics.
Multi-class point cloud completion networks for 3D cardiac anatomy reconstruction from cine magnetic resonance images
Cine magnetic resonance imaging (MRI) is the current gold standard for the assessment of cardiac anatomy and function.
3D Shape-Based Myocardial Infarction Prediction Using Point Cloud Classification Networks
Myocardial infarction (MI) is one of the most prevalent cardiovascular diseases with associated clinical decision-making typically based on single-valued imaging biomarkers.
Towards Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference
In this work, we investigate the feasibility of inferring myocardial tissue properties from the electrocardiogram (ECG) within a CDT platform.
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
Deep Computational Model for the Inference of Ventricular Activation Properties
Cardiac digital twins can provide non-invasive characterizations of cardiac functions for individual patients, and therefore are promising for the patient-specific diagnosis and therapy stratification.
The Liver Tumor Segmentation Benchmark (LiTS)
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.
