PROSurvival - Überlebensvorhersage für Prostatakrebspatienten mithilfe von föderiertem maschinellem Lernen und prädiktiven morphologischen Mustern
gemeinsamer Schlussbericht
| dc.contributor.author | Eichelberg, Marco | |
| dc.contributor.author | Wolters, Timo | |
| dc.contributor.author | Xu, Tingyan | |
| dc.contributor.author | Lotz, Johannes | |
| dc.contributor.author | Nicke, Till | |
| dc.contributor.author | Schäfer, Jan Raphael | |
| dc.contributor.author | Schwen, Ole | |
| dc.contributor.author | Thielke, Felix | |
| dc.contributor.author | Bein, Julia | |
| dc.contributor.author | Chun, Felix | |
| dc.contributor.author | Flinner, Nadine | |
| dc.contributor.author | Laib, Anna | |
| dc.contributor.author | Lecomte, Hugo | |
| dc.contributor.author | Mayer, Robin Sebastian | |
| dc.contributor.author | Wild, Peter | |
| dc.contributor.author | Bisson, Tom | |
| dc.contributor.author | Carvalho, Rita do Espirito Santo | |
| dc.contributor.author | Dogan O, Isil | |
| dc.contributor.author | Elezkurtaj, Sefer | |
| dc.contributor.author | Kiehl, Tim-Rasmus | |
| dc.contributor.author | Zerbe, Norman | |
| dc.date.accessioned | 2025-12-01T10:28:44Z | |
| dc.date.available | 2025-12-01T10:28:44Z | |
| dc.date.issued | 2025-12-01 | |
| dc.description.version | publishedVersion | |
| dc.identifier.uri | https://oa.tib.eu/renate/handle/123456789/26801 | |
| dc.identifier.uri | https://doi.org/10.34657/26038 | |
| dc.language.iso | ger | |
| dc.publisher | Hannover : Technische Informationsbibliothek | |
| dc.relation.affiliation | OFFIS e.V. | |
| dc.relation.affiliation | Fraunhofer-Institut für Digitale Medizin MEVIS | |
| dc.relation.affiliation | Johann Wolfgang Goethe-Universität Frankfurt/Main | |
| dc.relation.affiliation | Charité - Universitätsmedizin Berlin | |
| dc.rights.license | CC BY-ND 3.0 DE | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nd/3.0/de/ | |
| dc.subject.ddc | 000 | Informatik, Information und Wissen, allgemeine Werke | |
| dc.title | PROSurvival - Überlebensvorhersage für Prostatakrebspatienten mithilfe von föderiertem maschinellem Lernen und prädiktiven morphologischen Mustern | ger |
| dc.title.subtitle | gemeinsamer Schlussbericht | |
| dc.type | Report | |
| dcterms.extent | 29 Seiten | |
| dtf.duration | 01.11.2022-31.03.2025 | |
| dtf.funding.funder | BMFTR | |
| dtf.funding.program | 01KD2213A | |
| dtf.funding.program | 01KD2213B | |
| dtf.funding.program | 01KD2213C | |
| dtf.funding.program | 01KD2213D | |
| dtf.funding.verbundnummer | 01252122 | |
| dtf.version | 1 | |
| tib.accessRights | openAccess |
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