Non-invasive prediction of lymph node risk in oral cavity cancer patients using a combination of supervised and unsupervised machine learning algorithms

A. Traversoa*, A. Hosni-Abdalaty, M. Hasan, T. Tadic, T. Patel, M. Giuliani, J. Kim, J. Ringash, J. Cho, S. Bratman, A. Bayley, J. Waldron, B. O'Sullivan, J. Irish, D. Chepeha, J. De Almeida, D. Goldstein, D. Jaffray, L. Wee, A. DekkerA. Hope, A. Krol, B.S. Gimi

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

Original languageEnglish
Title of host publicationMEDICAL IMAGING 2020: BIOMEDICAL APPLICATIONS IN MOLECULAR, STRUCTURAL, AND FUNCTIONAL IMAGING
EditorsA Krol, BS Gimi
PublisherSPIE-INT SOC OPTICAL ENGINEERING
Number of pages7
Volume11317
ISBN (Print)9781510634022
DOIs
Publication statusPublished - 2021
EventSPIE Medical Imaging Conference: Biomedical Applications in Molecular, Structural, and Functional Imaging - Houston, United States
Duration: 15 Feb 202020 Feb 2020

Publication series

SeriesProgress in Biomedical Optics and Imaging
Number113172C
Volume11317
ISSN1605-7422

Conference

ConferenceSPIE Medical Imaging Conference
Abbreviated titleMI106
Country/TerritoryUnited States
CityHouston
Period15/02/2020/02/20

Keywords

  • magnetic resonance imaging
  • machine learning
  • oral cavity cancers
  • radiomics
  • RADIOMICS

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