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End-to-end prediction of clinical outcomes in head and neck squamous cell carcinoma with foundation model-based multiple instance learning

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In
BMC artificial intelligence 1(1),

ISSN3005-1924

Online
DOI: 10.1186/s44398-025-00003-8


Einrichtungen

  1. Lehrstuhl für Maschinelles Lernen in der Radiologie (952210)
  2. Klinik und Lehrstuhl für Diagnostische und Interventionelle Radiologie (532010-2)



Dokumenttyp
Journal Article

Format
online

Sprache
English

Externe Identnummern
PubMed: pmid:40605934

Interne Identnummern
RWTH-CONV-255295
Datensatz-ID: 1030957

Lizenzstatus der Zeitschrift

 GO

Related:

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Preprint  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;
End-to-end prediction of clinical outcomes in head and neck squamous cell carcinoma with foundation model-based multiple instance learning
23 Seiten () [10.1101/2025.01.22.25320517]  GO OpenAccess  Download fulltext Files BibTeX | EndNote: XML, Text | RIS

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Documents in print
532010\-2
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 Record created 2026-03-11, last modified 2026-09-03
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