  Although neural networks have proven very successful in a number of medical
image analysis applications, their use remains difficult when targeting subtle
tasks such as the identification of barely visible brain lesions, especially
given the lack of annotated datasets. Good candidate approaches are patch-based
unsupervised pipelines which have both the advantage to increase the number of
input data and to capture local and fine anomaly patterns distributed in the
image, while potential inconveniences are the loss of global structural
information. We illustrate this trade-off on Parkinson's disease (PD) anomaly
detection comparing the performance of two anomaly detection models based on a
spatial auto-encoder (AE) and an adaptation of a patch-fed siamese auto-encoder
(SAE). On average, the SAE model performs better, showing that patches may
indeed be advantageous.
