no code implementations • 25 Feb 2023 • Lemuel Puglisi, Frederik Barkhof, Daniel C. Alexander, Geoffrey JM Parker, Arman Eshaghi, Daniele Ravì
Our framework is a semi-self-supervised contrastive deep learning approach with three main innovations.
no code implementations • 7 Jun 2022 • Daniele Ravi, Frederik Barkhof, Daniel C. Alexander, Lemuel Puglisi, Geoffrey JM Parker, Arman Eshaghi
To tackle this problem, we propose a framework with four main components: 1) artefact generators inspired by magnetic resonance physics to corrupt brain MRI scans and augment a training dataset, 2) abstract and engineered features to represent images compactly, 3) a feature selection process depending on the artefact class to improve classification, and 4) SVM classifiers to identify artefacts.
no code implementations • 19 May 2020 • Yipeng Hu, Joseph Jacob, Geoffrey JM Parker, David J. Hawkes, John R. Hurst, Danail Stoyanov
The COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus 2, emerged into a world being rapidly transformed by artificial intelligence (AI) based on big data, computational power and neural networks.