Work

Deep Learning for Low-Field to High-Field MR: Image Quality Transfer with Probabilistic Decimation Simulator

Year: 2019

Type: conference-paper

Source: Lecture notes in computer science

DOI: 10.1007/978-3-030-33843-5_6

Authors: Hongxiang Lin, Matteo Figini, Ryutaro Tanno, Stefano B. Blumberg, Enrico Kaden, Godwin Ogbole, Biobele J. Brown, Felice D’Arco, David W. Carmichael, Ikeoluwa Lagunju, Helen J. Cross, Delmiro Fernandez-Reyes, Daniel C. Alexander

Institutions: University College London, Microsoft Research (United Kingdom), University of Ibadan, Great Ormond Street Hospital, King's College London

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, National Institutes of Health, Engineering and Physical Sciences Research Council

Awards: Learning MRI and histology image mappings for cancer diagnosis and prognosis, G186047356, G2810537366, Medical image computing for next-generation healthcare technology, G3953918217, G3958697817, G4266322423, G5651206546, G6127694706, G844244329, Enabling Clinical Decisions From Low-power MRI In Developing Nations Through Image Quality Transfer, G8849905504, G9000089182, G902693135

OpenAlex ID: W2982362131

FWCI: 0.815

Cites: 19

Cited by: 29

Related to: 10