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 building a subject-specific model of healthy
appearance to which a subject's image can be compared to detect anomalies. In
the literature, it is common for anomaly detection to rely on analysing the
residual image between the subject's image and its pseudo-healthy
reconstruction. This approach however has limitations partly due to the
pseudo-healthy reconstructions being imperfect and to the lack of natural
thresholding mechanism. Our proposed method, inspired by Z-scores, leverages
the healthy population variability to overcome these limitations. Our
experiments conducted on FDG PET scans from the ADNI database demonstrate the
effectiveness of our approach in accurately identifying Alzheimer's disease
related anomalies.