Work

Generalised Super Resolution for Quantitative MRI Using Self-supervised Mixture of Experts

Year: 2021

Type: conference-paper

Source: Lecture notes in computer science

DOI: 10.1007/978-3-030-87231-1_5

Authors: Hongxiang Lin, Yukun Zhou, Paddy J. Slator, Daniel C. Alexander

Institutions: Zhejiang Lab, University College London, NIHR Moorfields Biomedical Research Centre, Moorfields Eye Hospital

Language: English

Topic: Advanced Image Processing Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Institute for Health and Care Research, University College London Hospitals NHS Foundation Trust, Engineering and Physical Sciences Research Council

Awards: G2810537366, Medical image computing for next-generation healthcare technology, Assessing Placental Structure and Function by Unified Fluid Mechanical Modelling and in-vivo MRI, Enabling Clinical Decisions From Low-power MRI In Developing Nations Through Image Quality Transfer, G902693135

OpenAlex ID: W3200922958

FWCI: 0.409

Cites: 38

Cited by: 5

Related to: 10