  The identification and quantification of markers in medical images is
critical for diagnosis, prognosis and management of patients in clinical
practice. Supervised- or weakly supervised training enables the detection of
findings that are known a priori. It does not scale well, and a priori
definition limits the vocabulary of markers to known entities reducing the
accuracy of diagnosis and prognosis. Here, we propose the identification of
anomalies in large-scale medical imaging data using healthy examples as a
reference. We detect and categorize candidates for anomaly findings untypical
for the observed data. A deep convolutional autoencoder is trained on healthy
retinal images. The learned model generates a new feature representation, and
the distribution of healthy retinal patches is estimated by a one-class support
vector machine. Results demonstrate that we can identify pathologic regions in
images without using expert annotations. A subsequent clustering categorizes
findings into clinically meaningful classes. In addition the learned features
outperform standard embedding approaches in a classification task.
