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Sebastien Ourselin

Researcher at King's College London

Publications -  1221
Citations -  51730

Sebastien Ourselin is an academic researcher from King's College London. The author has contributed to research in topics: Segmentation & Medicine. The author has an hindex of 91, co-authored 1116 publications receiving 34683 citations. Previous affiliations of Sebastien Ourselin include University College Hospital & University of London.

Papers
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Journal ArticleDOI

Risk of COVID-19 among front-line health-care workers and the general community: a prospective cohort study.

Long H. Nguyen, +81 more
- 01 Sep 2020 - 
TL;DR: In the UK and the USA, risk of reporting a positive test for COVID-19 was increased among front-line health-care workers, and adequacy of PPE, clinical setting, and ethnic background were also important factors.
Book ChapterDOI

Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations

Carole H. Sudre, +7 more
TL;DR: In this paper, the authors investigate the behavior of these loss functions and their sensitivity to learning rate tuning in the presence of different rates of label imbalance across 2D and 3D segmentation tasks.
Journal ArticleDOI

Attributes and predictors of long COVID.

Carole H. Sudre, +35 more
- 10 Mar 2021 - 
TL;DR: In this paper, the authors analyzed data from 4,182 incident cases of COVID-19 in which individuals self-reported their symptoms prospectively in the COVID Symptom Study app.
Posted ContentDOI

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Spyridon Bakas, +438 more
- 05 Nov 2018 - 
TL;DR: This study assesses the state-of-the-art machine learning methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018, and investigates the challenge of identifying the best ML algorithms for each of these tasks.
Journal ArticleDOI

Real-time tracking of self-reported symptoms to predict potential COVID-19.

Cristina Menni, +21 more
- 11 May 2020 - 
TL;DR: Analysis of data from a smartphone-based app designed for large-scale tracking of potential COVID-19 symptoms, used by over 2.5 million participants in the United Kingdom and United States, shows that loss of taste and smell sensations is predictive of potential SARS-CoV-2 infection.