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DeepBrainPrint: A Novel Contrastive Framework for Brain MRI Re-Identification
Our framework is a semi-self-supervised contrastive deep learning approach with three main innovations.
An efficient semi-supervised quality control system trained using physics-based MRI-artefact generators and adversarial training
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.
The challenges of deploying artificial intelligence models in a rapidly evolving pandemic
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.
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