Towards a unified approach for unsupervised brain MRI Motion Artefact Detection with few shot Anomaly Detection

Niamh Belton, Misgina Tsighe Hagos, Aonghus Lawlor, Kathleen M. Curran

Abstract: Highlights•This work investigates unsupervised approaches to MAD and MA severity classification.•As the models are unsupervised, the requirement to acquire MRIs with MAs is eliminated.•FewSOME achieves AUCs > 0.9 on two datasets having trained on a small dataset of 30 MRIs.•This work shows how the anomaly scores correlate with MA severity.•This work proposes an ‘anomaly-aware’ scoring function.•This work provides a standardised protocol that can be used as a benchmark for future MAD analyses.

External IDs:dblp:journals/cmig/BeltonHLC24