Zusammenfassung
Unsupervised anomaly detection is often attributed great promise, especially for rare conditions and fast adaptation to novel conditions or imaging techniques without the need for explicitly labeled data. However, most previous works study different methods in a constrained research setting with a limited number of common types of pathologies. Here, we want to explore a more realistic setting and target the incidental findings in a large-scale population study with 10000 participants using a recent anomaly detection approach. Despite the difficulties in selecting a proper training set in such scenarios, we were able to produce promising quantitative results and detected 31 anomalies which were not reported previously. Evaluation by a radiologist revealed remaining open challenges when it comes to the detection of less conspicuous anomalies.
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© 2022 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Zimmerer, D., Paech, D., Lüth, C., Petersen, J., Köhler, G., Maier-Hein, K. (2022). Unsupervised Anomaly Detection in the Wild. In: Maier-Hein, K., Deserno, T.M., Handels, H., Maier, A., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2022. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-36932-3_6
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DOI: https://doi.org/10.1007/978-3-658-36932-3_6
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