Work

Disentangled representation learning in cardiac image analysis

Year: 2019

Type: article

Source: Medical Image Analysis

DOI: 10.1016/j.media.2019.101535

Authors: Agisilaos Chartsias, Thomas Joyce, Giorgos Papanastasiou, Scott Semple, Michelle Williams, David E. Newby, Rohan Dharmakumar, Sotirios A. Tsaftaris

Institutions: University of Edinburgh, British Heart Foundation Centre for Cardiovascular Science, The Queen's Medical Research Institute, Cedars-Sinai Medical Center, The Alan Turing Institute

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: British Heart Foundation, Royal Academy of Engineering, National Institutes of Health, Engineering and Physical Sciences Research Council, National Heart, Lung, and Blood Institute

Awards: G137526617, G2465337746, G2574353342, CardiacA.I.: Machine learning for the analysis of multimodal cardiac MR images used in the diagnosis of coronary heart disease, Accurate, Needle-Free, MRI-based Detection of Ischemic Heart Disease without Contrast Agents, G8822802767, G9000195780

OpenAlex ID: W2959170286

FWCI: 10.65

Cites: 62

Cited by: 193

Related to: 10