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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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Umfang23 Seiten

Online
DOI: 10.18154/RWTH-CONV-255140
DOI: 10.1101/2025.01.22.25320517

URL: https://publications.rwth-aachen.de/record/1030802/files/1030802.pdf

Einrichtungen

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


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Dokumenttyp
Preprint

Format
online

Sprache
English

Externe Identnummern
OpenAlex: W4406818968

Interne Identnummern
RWTH-CONV-255140
Datensatz-ID: 1030802

Beteiligte Länder
Germany, Spain, USA

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Related:

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Journal Article  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;
End-to-end prediction of clinical outcomes in head and neck squamous cell carcinoma with foundation model-based multiple instance learning
BMC artificial intelligence 1(1), () [10.1186/s44398-025-00003-8]  GO OpenAccess BibTeX | EndNote: XML, Text | RIS

Creative Commons Attribution CC BY 4.0 ; OpenAccess
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 Record created 2026-03-11, last modified 2026-09-14
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