Work

Unsupervised Domain Adaptation in Brain Lesion Segmentation with Adversarial Networks

Year: 2017

Type: conference-paper

Source: Lecture notes in computer science

Parent work: Information Processing in Medical Imaging: 25th International Conference, IPMI 2017, Boone, NC, USA, June 25-30, 2017, Proceedings

DOI: 10.1007/978-3-319-59050-9_47

Authors: Konstantinos Kamnitsas, Christian Baumgartner, Christian Ledig, Virginia Newcombe, Joanna Simpson, Andrew Kane, David Menon, Aditya Nori, Antonio Criminisi, Daniel Rueckert, Ben Glocker

Institutions: Microsoft Research (United Kingdom), Imperial College London, University of Cambridge

Language: English

Topic: Domain Adaptation and Few-Shot Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Microsoft Research, Nvidia, National Institute for Health and Care Research, Imperial College London, European Commission, Engineering and Physical Sciences Research Council, NIHR Cambridge Biomedical Research Centre

Award: QuantifyTBI: A Machine Learning Approach to Automatic Segmentation and Quantification of Lesions in Traumatic Brain Injury Imaging

OpenAlex ID: W2562469482

FWCI: 22.35

Cites: 21

Cited by: 538

Related to: 10