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Titel:

Unsupervised Pathology Detection: A Deep Dive Into the State of the Art.

Dokumenttyp:
Journal Article
Autor(en):
Lagogiannis, Ioannis; Meissen, Felix; Kaissis, Georgios; Rueckert, Daniel
Abstract:
Deep unsupervised approaches are gathering increased attention for applications such as pathology detection and segmentation in medical images since they promise to alleviate the need for large labeled datasets and are more generalizable than their supervised counterparts in detecting any kind of rare pathology. As the Unsupervised Anomaly Detection (UAD) literature continuously grows and new paradigms emerge, it is vital to continuously evaluate and benchmark new methods in a common framework,...     »
Zeitschriftentitel:
IEEE Trans Med Imaging
Jahr:
2024
Band / Volume:
43
Heft / Issue:
1
Seitenangaben Beitrag:
241-252
Volltext / DOI:
doi:10.1109/TMI.2023.3298093
PubMed:
http://view.ncbi.nlm.nih.gov/pubmed/37506004
Print-ISSN:
0278-0062
TUM Einrichtung:
Institut für Diagnostische und Interventionelle Radiologie (Prof. Makowski); Institut für KI und Informatik in der Medizin (Prof. Rückert)
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