Radiomics at a glance: a few lessons learned from learning approaches

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1 Scopus citations

Abstract

Processing and modeling medical images have traditionally represented complex tasks requiring multidisciplinary collaboration. The advent of radiomics has assigned a central role to quantitative data analytics targeting medical image features algorithmically extracted from large volumes of images. Apart from the ultimate goal of supporting diagnostic, prognostic, and therapeutic decisions, radiomics is computationally attractive due to specific strengths: scalability, efficiency, and precision. Optimization is achieved by highly sophisticated statistical and machine learning algorithms, but it is especially deep learning that stands out as the leading inference approach. Various types of hybrid learning can be considered when building complex integrative approaches aimed to deliver gains in accuracy for both classification and prediction tasks. This perspective reviews some selected learning methods by focusing on both their significance for radiomics and their unveiled potential.

Original languageEnglish (US)
Article number2453
Pages (from-to)1-19
Number of pages19
JournalCancers
Volume12
Issue number9
DOIs
StatePublished - Sep 2020

Keywords

  • Integrative inference approaches
  • Machine learning
  • Predictive modeling
  • Radiomics

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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