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

Automated Motion Artefact Detection (MAD) in Magnetic Resonance Imaging (MRI) is a field of study that aims to automatically flag motion artefacts in order to prevent the requirement for a repeat scan. In this paper, we identify and tackle the three current challenges in the field of automated MAD; (1) reliance on fully-supervised training, meaning they require specific examples of Motion Artefacts (MA), (2) inconsistent use of benchmark datasets across different works and use of private datasets for testing and training of newly proposed MAD techniques and (3) a lack of sufficiently large datasets for MRI MAD. To address these challenges, we demonstrate how MAs can be identified by formulating the problem as an unsupervised Anomaly Detection (AD) task.

Cybersecurity considerations for radiology departments involved with artificial intelligence.

Radiology artificial intelligence (AI) projects involve the integration of integrating numerous medical devices, wireless technologies, data warehouses, and social networks. While cybersecurity threats are not new to healthcare, their prevalence has increased with the rise of AI research for applications in radiology, making them one of the major healthcare risks of 2021. Radiologists have extensive experience with the interpretation of medical imaging data but radiologists may not have the required level of awareness or training related to AI-specific cybersecurity concerns.