Anomaly detection in medical imaging is a challenging task in contexts where
abnormalities are not annotated. This problem can be addressed through
unsupervised anomaly detection (UAD) methods, which identify features that do
not match with a reference model of normal profiles. Artificial neural networks
have been extensively used for UAD but they do not generally achieve an optimal
trade-o$\hookleftarrow$ between accuracy and computational demand. As an
alternative, we investigate mixtures of probability distributions whose
versatility has been widely recognized for a variety of data and tasks, while
not requiring excessive design e$\hookleftarrow$ort or tuning. Their
expressivity makes them good candidates to account for complex multivariate
reference models. Their much smaller number of parameters makes them more
amenable to interpretation and e cient learning. However, standard estimation
procedures, such as the Expectation-Maximization algorithm, do not scale well
to large data volumes as they require high memory usage. To address this issue,
we propose to incrementally compute inferential quantities. This online
approach is illustrated on the challenging detection of subtle abnormalities in
MR brain scans for the follow-up of newly diagnosed Parkinsonian patients. The
identified structural abnormalities are consistent with the disease
progression, as accounted by the Hoehn and Yahr scale.

医学成像中的异常检测是一项具有挑战性的任务，可以通过无监督异常检测方法解决，该方法通过识别与正常基准模型不匹配的特征。该论文探讨了使用混合概率分布作为替代方法，以在准确性与计算需求之间取得最优化平衡，并且介绍了一个在线方法来解决大数据量下的内存使用问题，并以帕金森病患者脑部扫描的异常检测为例进行了验证。