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Obtaining models that capture imaging markers relevant for diseaseprogression and treatment monitoring is challenging. Models are typically basedon large amounts of data with annotated examples of known markers aiming atautomating detection. High annotation effort and the limitation to a vocabularyof known markers limit the power of such approaches. Here, we performunsupervised learning to identify anomalies in imaging data as candidates formarkers. We propose AnoGAN, a deep convolutional generative adversarial networkto learn a manifold of normal anatomical variability, accompanying a novelanomaly scoring scheme based on the mapping from image space to a latent space.Applied to new data, the model labels anomalies, and scores image patchesindicating their fit into the learned distribution. Results on opticalcoherence tomography images of the retina demonstrate that the approachcorrectly identifies anomalous images, such as images containing retinal fluidor hyperreflective foci.

Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery