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.

我们提出了一种受 Z 分数启发的方法，利用健康人群的变异性来识别阿尔茨海默病相关异常，实验证明了我们方法的有效性。