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Title:

Automated Pathology Detection and Patient Triage in Routinely Acquired Head Computed Tomography Scans.

Document type:
Journal Article
Author(s):
Finck, Tom; Schinz, David; Grundl, Lioba; Eisawy, Rami; Yigitsoy, Mehmet; Moosbauer, Julia; Pfister, Franz; Wiestler, Benedikt
Abstract:
OBJECTIVES: Anomaly detection systems can potentially uncover the entire spectrum of pathologies through deviations from a learned norm, meaningfully supporting the radiologist's workflow. We aim to report on the utility of a weakly supervised machine learning (ML) tool to detect pathologies in head computed tomography (CT) and adequately triage patients in an unselected patient cohort. MATERIALS AND METHODS: All patients having undergone a head CT at a tertiary care hospital in March 2020 were...     »
Journal title abbreviation:
Invest Radiol
Year:
2021
Journal volume:
56
Journal issue:
9
Pages contribution:
571-578
Fulltext / DOI:
doi:10.1097/RLI.0000000000000775
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/33813571
Print-ISSN:
0020-9996
TUM Institution:
Fachgebiet Neuroradiologie (Prof. Zimmer)
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