no code implementations • 6 May 2024 • Lemuel Puglisi, Daniel C. Alexander, Daniele Ravì
BrLP is designed to predict the evolution of diseases at the individual level on 3D brain MRIs.
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.