Anomaly detection

Research on anomaly detection spans a line of research aiming to extend the vocabulary of imaging markers beyond those we already know. We are developing several approaches to detect, segment and categorize anomalies. This page gives an overview on the publications and the code that is made available in this context.

Seeböck P., Orlando J.I., Schlegl T., Waldstein S., Bogunovic H., Klimscha S., Langs G., Schmidt-Erfurth U. Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT. IEEE Transactions on Medical Imaging. 2019. [arxiv]

Thomas Schlegl, Philipp Seeböck, Sebastian M. Waldstein, Georg Langs*, Ursula Schmidt-Erfurth. f-AnoGAN: Fast Unsupervised Anomaly Detection with Generative Adversarial Networks. in Medical Image Analysis. 2019, [pdf]

Code for f-AnoGAN is available: github

Seeböck P., Waldstein S., Klimscha S., Bogunovic H., Schlegl T., Gerendas B. S.,  Donner R., Schmidt-Erfurth U., Langs G. Unsupervised Identification of Disease Marker Candidates in Retinal OCT Imaging Data. IEEE Transactions on Medical Imaging. 2019. [arxiv]

T Schlegl, P , S M. Waldstein, U Schmidt-Erfurth, G Langs. Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery. In International Conference on Information Processing in Medical Imaging, pp. 146-157. Springer, Cham, 2017. [arxiv]