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The STOIC2021 COVID-19 AI challenge: Applying reusable training methodologies to private data

dc.contributor.authorBoulogne, Luuk H.
dc.contributor.authorLorenz, Julian
dc.contributor.authorKienzle, Daniel
dc.contributor.authorSchoen, Robin
dc.contributor.authorLudwig, Katja
dc.contributor.authorLienhart, Rainer
dc.contributor.authorJegou, Simon
dc.contributor.authorLi, Guang
dc.contributor.authorChen, Cong
dc.contributor.authorWang, Qi
dc.contributor.authorShi, Derik
dc.contributor.authorManiparambil, Mayug
dc.contributor.authorMueller, Dominik
dc.contributor.authorMertes, Silvan
dc.contributor.authorSchroeter, Niklas
dc.contributor.authorHellmann, Fabio
dc.contributor.authorElia, Miriam
dc.contributor.authorDirks, Ine
dc.contributor.authorBossa, Matias Nicolas
dc.contributor.authorBerenguer, Abel Diaz
dc.contributor.authorMukherjee, Tanmoy
dc.contributor.authorVandemeulebroucke, Jef
dc.contributor.authorSahli, Hichem
dc.contributor.authorDeligiannis, Nikolaos
dc.contributor.authorGonidakis, Panagiotis
dc.contributor.authorHuynh, Ngoc Dung
dc.contributor.authorRazzak, Imran
dc.contributor.authorBouadjenek, Reda
dc.contributor.authorVerdicchio, Mario
dc.contributor.authorBorrelli, Pasquale
dc.contributor.authorAiello, Marco
dc.contributor.authorMeakin, James A.
dc.contributor.authorLemm, Alexander
dc.contributor.authorRuss, Christoph
dc.contributor.authorIonasec, Razvan
dc.contributor.authorParagios, Nikos
dc.contributor.authorvan Ginneken, Bram
dc.contributor.authorRevel, Marie-Pierre
dc.date.accessioned2025-01-23T10:28:07Z
dc.date.available2024-07-05T18:51:14Z
dc.date.available2025-01-23T10:28:07Z
dc.date.issued2024
dc.identifier.issn1361-8415
dc.identifier.otherWOS:001257371000001
dc.identifier.urihttps://imec-publications.be/handle/20.500.12860/44127.3
dc.sourceWOS
dc.titleThe STOIC2021 COVID-19 AI challenge: Applying reusable training methodologies to private data
dc.typeJournal article
dc.contributor.imecauthorBossa, Matias Nicolas
dc.contributor.imecauthorBerenguer, Abel Diaz
dc.contributor.imecauthorMukherjee, Tanmoy
dc.contributor.imecauthorVandemeulebroucke, Jef
dc.contributor.imecauthorSahli, Hichem
dc.contributor.imecauthorDeligiannis, Nikolaos
dc.contributor.orcidimecMukherjee, Tanmoy::0000-0002-9540-5398
dc.contributor.orcidimecVandemeulebroucke, Jef::0000-0001-5714-3254
dc.contributor.orcidimecSahli, Hichem::0000-0002-1774-2970
dc.contributor.orcidimecDeligiannis, Nikolaos::0000-0001-9300-5860
dc.date.embargo2024-06-14
dc.identifier.doi10.1016/j.media.2024.103230
dc.source.numberofpages17
dc.source.peerreviewyes
dc.source.beginpageArt. 103230
dc.source.endpageN/A
dc.source.journalMEDICAL IMAGE ANALYSIS
dc.identifier.pmidMEDLINE:38875741
dc.source.issueOctober
dc.source.volume97
imec.availabilityPublished - open access
dc.description.wosFundingTextThe European Regional Development Fund had no role in the study design, data collection, data analysis, data interpretation, or writing of the manuscript. Amazon Web Services funded algorithm evaluation, algorithm training for the Final phase, and prizes to the best performing teams.


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320.500.12860/44127.3*2025-01-23T10:20:09Zvalidation by library/open access desk
120.500.12860/441272024-07-05T18:51:14Z

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