3D Shape-Based Myocardial Infarction Prediction Using Point Cloud Classification Networks
Author:
Affiliation:
1. University of Oxford,Institute of Biomedical Engineering,Department of Engineering Science,Oxford,UK,OX3 7DQ
Funder
Stiftung der Deutschen Wirtschaft
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10339936/10339939/10340878.pdf?arnumber=10340878
Reference28 articles.
1. Global Epidemiology of Ischemic Heart Disease: Results from the Global Burden of Disease Study
2. Role of Cardiac Magnetic Resonance to Improve Risk Prediction following Acute ST-elevation Myocardial Infarction
3. Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?
4. Automatic Cardiac Disease Assessment on cine-MRI via Time-Series Segmentation and Domain Specific Features
5. Automatic Segmentation and Disease Classification Using Cardiac Cine MR Images
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1. Modeling 3D Cardiac Contraction and Relaxation With Point Cloud Deformation Networks;IEEE Journal of Biomedical and Health Informatics;2024-08
2. Generating Virtual Populations of 3D Cardiac Anatomies with Snowflake-Net;Lecture Notes in Computer Science;2024
3. Detecting and Classifying Myocardial Infarction in Echocardiogram Frames With an Enhanced CNN Algorithm and ECV-3D Network;IEEE Access;2024
4. Multi-objective Point Cloud Autoencoders for Explainable Myocardial Infarction Prediction;Lecture Notes in Computer Science;2023
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