Search Results for author:
Found 5 papers, 2 papers with code
Interpretable Multimodal Learning for Cardiovascular Hemodynamics Assessment
We extract spatio-temporal features from CMR scans using tensor-based learning.
Multimodal Variational Autoencoder for Low-cost Cardiac Hemodynamics Instability Detection
In response to these limitations, we propose a novel multimodal variational autoencoder ($\text{CardioVAE}_\text{X, G}$) to integrate low-cost chest X-ray (CXR) and electrocardiogram (ECG) modalities with pre-training on a large unlabeled dataset.
MeDSLIP: Medical Dual-Stream Language-Image Pre-training for Fine-grained Alignment
Moreover, a novel vision-language Prototypical Contr-astive Learning (ProtoCL) method is adopted in MeDSLIP to enhance the alignment within the anatomical and pathological streams.
Deep learning automated quantification of lung disease in pulmonary hypertension on CT pulmonary angiography: A preliminary clinical study with external validation
This study aims to develop an artificial intelligence (AI) deep learning model for lung texture classification in CT Pulmonary Angiography (CTPA), and evaluate its correlation with clinical assessment methods.
Tensor-based Multimodal Learning for Prediction of Pulmonary Arterial Wedge Pressure from Cardiac MRI
In this work, we develop a tensor learning-based pipeline for identifying PAWP from multimodal cardiac Magnetic Resonance Imaging (MRI).
