  The identification and quantification of markers in medical images is
critical for diagnosis, prognosis, and disease management. Supervised machine
learning enables the detection and exploitation of findings that are known a
priori after annotation of training examples by experts. However, supervision
does not scale well, due to the amount of necessary training examples, and the
limitation of the marker vocabulary to known entities. In this proof-of-concept
study, we propose unsupervised identification of anomalies as candidates for
markers in retinal Optical Coherence Tomography (OCT) imaging data without a
constraint to a priori definitions. We identify and categorize marker
candidates occurring frequently in the data, and demonstrate that these markers
show predictive value in the task of detecting disease. A careful qualitative
analysis of the identified data driven markers reveals how their quantifiable
occurrence aligns with our current understanding of disease course, in early-
and late age-related macular degeneration (AMD) patients. A multi-scale deep
denoising autoencoder is trained on healthy images, and a one-class support
vector machine identifies anomalies in new data. Clustering in the anomalies
identifies stable categories. Using these markers to classify healthy-, early
AMD- and late AMD cases yields an accuracy of 81.40%. In a second binary
classification experiment on a publicly available data set (healthy vs.
intermediate AMD) the model achieves an area under the ROC curve of 0.944.
