A comparative study of machine learning methods for time-to-event survival data for radiomics risk modelling

Leger, Stefan; Zwanenburg, Alex; Pilz, Karoline; Lohaus, Fabian; Linge, Annett; Zoephel, Klaus; Kotzerke, Joerg; Schreiber, Andreas; Tinhofer, Inge; Budach, Volker; Sak, Ali; Stuschke, Martin; Balermpas, Panagiotis; Roedel, Claus; Ganswindt, Ute; Belka, Claus; Pigorsch, Steffi; Combs, Stephanie E.; Moennich, David; Zips, Daniel; Krause, Mechthild; Baumann, Michael; Troost, Esther G. C.; Loeck, Steffen; Richter, Christian

A comparative study of machine learning methods for time-to-event survival data for radiomics risk modelling

Autor(en):

Leger, Stefan; Zwanenburg, Alex; Pilz, Karoline; Lohaus, Fabian; Linge, Annett; Zoephel, Klaus; Kotzerke, Joerg; Schreiber, Andreas; Tinhofer, Inge; Budach, Volker; Sak, Ali; Stuschke, Martin; Balermpas, Panagiotis; Roedel, Claus; Ganswindt, Ute; Belka, Claus; Pigorsch, Steffi; Combs, Stephanie E.; Moennich, David; Zips, Daniel; Krause, Mechthild; Baumann, Michael; Troost, Esther G. C.; Loeck, Steffen; Richter, Christian

Tübinger Autor(en):

Zips, Daniel
Mönnich, David

Erschienen in:

Scientific Reports (2017), Bd. 7, Article 13206

Verlagsangabe:

Nature Publishing Group

Sprache:

Englisch

Referenz zum Volltext:

http://dx.doi.org/10.1038/s41598-017-13448-3

ISSN:

2045-2322