Towards frugal unsupervised detection of subtle abnormalities in medical
  imaging
Geoffroy Oudoumanessah Carole Lartizien Michel Dojat Florence Forbes
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.