7 papers · 1 filter
Metrics that matter: Evaluating image quality metrics for medical image generation
Yash Deo, Yan Jia, Toni Lassila +5
Evaluating generative models for synthetic medical imaging is crucial yet challenging, especially given the high standards of fidelity, anatomical accuracy, and safety required for…
Multi-view Hybrid Graph Convolutional Network for Volume-to-mesh Reconstruction in Cardiovascular MRI
Nicolás Gaggion, Benjamin A. Matheson, Yan Xia +6
Cardiovascular magnetic resonance imaging is emerging as a crucial tool to examine cardiac morphology and function. Essential to this endeavour are anatomical 3D surface and volume…
Predicting risk of cardiovascular disease using retinal OCT imaging
Cynthia Maldonado-Garcia, Rodrigo Bonazzola, Enzo Ferrante +4
Cardiovascular diseases (CVD) are the leading cause of death globally. Non-invasive, cost-effective imaging techniques play a crucial role in early detection and prevention of CVD.…
A Symmetric Dynamic Learning Framework for Diffeomorphic Medical Image Registration
Jinqiu Deng, Ke Chen, Mingke Li +4
Diffeomorphic image registration is crucial for various medical imaging applications because it can preserve the topology of the transformation. This study introduces DCCNN-LSTM-Re…
Integrating Deep Learning with Fundus and Optical Coherence Tomography for Cardiovascular Disease Prediction
Cynthia Maldonado-Garcia, Arezoo Zakeri, Alejandro F Frangi +1
Early identification of patients at risk of cardiovascular diseases (CVD) is crucial for effective preventive care, reducing healthcare burden, and improving patients' quality of l…
Statistical Distance-Guided Unsupervised Domain Adaptation for Automated Multi-Class Cardiovascular Magnetic Resonance Image Quality Assessment
Shahabedin Nabavi, Kian Anvari Hamedani, Mohsen Ebrahimi Moghaddam +2
This study proposes an attention-based statistical distance-guided unsupervised domain adaptation model for multi-class cardiovascular magnetic resonance (CMR) image quality assess…