Machine learning helps identifying volume-confounding effects in radiomics

Alberto Traverso, Michal Kazmierski, Ivan Zhovannik, Mattea Welch, Leonard Wee, David Jaffray, Andre Dekker, Andrew Hope

Research output: Contribution to journalArticleAcademicpeer-review

Original languageEnglish
Pages (from-to)24-30
Number of pages7
JournalPhysica Medica: European journal of medical physics
Volume71
DOIs
Publication statusPublished - Mar 2020
EventInternational Conference on the Use of Computers in Radiation Therapy (ICCR) / International Conference on Monte Carlo Techniques for Medical Applications (MCMA) - Montreal, Canada
Duration: 17 Jun 201921 Jun 2019

Keywords

  • Radiomics
  • Machine learning
  • Predictions
  • Lung
  • Head and neck
  • TUMOR VOLUME
  • HETEROGENEITY

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