Diagnostic validation of a deep learning nodule detection algorithm in low-dose chest CT: determination of optimized dose thresholds in a virtual screening scenario.
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| Title: | Diagnostic validation of a deep learning nodule detection algorithm in low-dose chest CT: determination of optimized dose thresholds in a virtual screening scenario. |
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| Authors: | Peters AA; Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Bern University Hospital, University of Bern, Inselspital Bern, 3010, Switzerland. alan.peters@insel.ch., Huber AT; Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Bern University Hospital, University of Bern, Inselspital Bern, 3010, Switzerland., Obmann VC; Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Bern University Hospital, University of Bern, Inselspital Bern, 3010, Switzerland., Heverhagen JT; Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Bern University Hospital, University of Bern, Inselspital Bern, 3010, Switzerland.; Department of BioMedical Research, Experimental Radiology, University of Bern, 3008, Bern, Switzerland.; Department of Radiology, The Ohio State University, Columbus, OH, USA., Christe A; Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Bern University Hospital, University of Bern, Inselspital Bern, 3010, Switzerland., Ebner L; Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Bern University Hospital, University of Bern, Inselspital Bern, 3010, Switzerland. |
| Source: | European radiology [Eur Radiol] 2022 Jun; Vol. 32 (6), pp. 4324-4332. Date of Electronic Publication: 2022 Jan 21. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Springer International Country of Publication: Germany NLM ID: 9114774 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1432-1084 (Electronic) Linking ISSN: 09387994 NLM ISO Abbreviation: Eur Radiol Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1432-1084 |
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| DOI: | 10.1007/s00330-021-08511-7 |
