Work

Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation

Year: 2018

Type: conference-paper

Source: Lecture notes in computer science

Parent work: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

DOI: 10.1007/978-3-319-75238-9_38

Authors: K. Kamnitsas, W. Bai, E. Ferrante, S. McDonagh, M. Sinclair, N. Pawlowski, M. Rajchl, M. Lee, B. Kainz, D. Rueckert, B. Glocker

Institution: Imperial College London

Language: English

Topic: Brain Tumor Detection and Classification

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Microsoft Research, Nvidia, Imperial College London, European Commission, AXA Research Fund, Engineering and Physical Sciences Research Council

Awards: G1961329086, SmartHeart: Next-generation cardiovascular healthcare via integrated image acquisition, reconstruction, analysis and learning, EPSRC Centre for Doctoral Training in High Performance Embedded and Distributed Systems, QuantifyTBI: A Machine Learning Approach to Automatic Segmentation and Quantification of Lesions in Traumatic Brain Injury Imaging, Exploration of machine learning for pre-emptive scheduling in single-instruction multiple-data mega-kernel designs

OpenAlex ID: W2767623272

FWCI: 21.46

Cites: 31

Cited by: 506

Related to: 10