Unsupervised anomaly detection in the wild
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 l...
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| Main Authors: | , , , , , |
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| Format: | Article (Journal) Chapter/Article |
| Language: | German |
| Published: |
05 April 2022
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| In: |
Bildverarbeitung für die Medizin 2022
Year: 2022, Pages: 26-31 |
| DOI: | 10.1007/978-3-658-36932-3_6 |
| Online Access: | Resolving-System, kostenfrei, Volltext: https://doi.org/10.1007/978-3-658-36932-3_6
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| Author Notes: | David Zimmerer, Daniel Paech, Carsten Lüth, Jens Petersen, Gregor Köhler, Klaus Maier-Hein |
| Summary: | 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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| Item Description: | Gesehen am 30.09.2025 |
| Physical Description: | Online Resource |
| ISBN: | 9783658369323 |
| DOI: | 10.1007/978-3-658-36932-3_6 |