Towards safe and reliable deep learning for lung nodule malignancy estimation using out-of-distribution detection.
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| Title: | Towards safe and reliable deep learning for lung nodule malignancy estimation using out-of-distribution detection. |
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| Authors: | Peeters D; Diagnostic Imaging Analysis Group, Medical Imaging Department, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands. Electronic address: dre.peeters@radboudumc.nl., Venkadesh KV; Diagnostic Imaging Analysis Group, Medical Imaging Department, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands., Dinnessen R; Diagnostic Imaging Analysis Group, Medical Imaging Department, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands., Saghir Z; Department of Medicine, Section of Pulmonary Medicine, Herlev-Gentofte Hospital, Hellerup, Denmark; Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark., Scholten ET; Diagnostic Imaging Analysis Group, Medical Imaging Department, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands., Vliegenthart R; Department of Radiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9700RB, Groningen, the Netherlands., Prokop M; Diagnostic Imaging Analysis Group, Medical Imaging Department, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands; Department of Radiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9700RB, Groningen, the Netherlands., Jacobs C; Diagnostic Imaging Analysis Group, Medical Imaging Department, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands. |
| Source: | Computers in biology and medicine [Comput Biol Med] 2025 Mar; Vol. 186, pp. 109633. Date of Electronic Publication: 2024 Dec 30. |
| Publication Type: | Journal Article; Research Support, Non-U.S. Gov't |
| Journal Info: | Publisher: Elsevier Country of Publication: United States NLM ID: 1250250 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-0534 (Electronic) Linking ISSN: 00104825 NLM ISO Abbreviation: Comput Biol Med Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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