most citedLeveraging healthy population variability in deep learning unsupervised anomaly detection in brain FDG PET

3 citations · 3 across the 2 of their papers we have counts for

collaborators

2 papers

cs.CV2024

Confidence intervals uncovered: Are we ready for real-world medical imaging AI?

Evangelia Christodoulou, Annika Reinke, Rola Houhou +19

Medical imaging is spearheading the AI transformation of healthcare. Performance reporting is key to determine which methods should be translated into clinical practice. Frequently…

eess.IV2023★ 3 cited

Leveraging healthy population variability in deep learning unsupervised anomaly detection in brain FDG PET

Maëlys Solal, Ravi Hassanaly, Ninon Burgos

Unsupervised anomaly detection is a popular approach for the analysis of neuroimaging data as it allows to identify a wide variety of anomalies from unlabelled data. It relies on b…