Precision medicine in digital pathology via image analysis and machine learning

Peter D. Caie, Neofytos Dimitriou, Ognjen Arandjelović

Research output: Chapter in Book/Report/Conference proceedingChapter

17 Citations (Scopus)

Abstract

In this chapter, we present the concept that digital pathology and artificial intelligence can add value and speed to a pathologist's diagnosis while striving toward precision medicine. We describe how image analysis and machine learning can segment images and compute object- and spatial-based data prior to analysis. This data analysis can identify patients who are at a high risk of succumbing to a disease, who may need more detailed clinical follow-up, or who will respond to specific therapy. We also describe how deep learning algorithms can learn complex morphological patterns from both human- and data-led input in order to perform diagnostic or prognostic tasks. Finally, we discuss the theory behind some commonly used machine learning algorithms and how they may attain regulatory approval.

Original languageEnglish
Title of host publicationArtificial intelligence and deep learning in pathology
EditorsStanley Cohen
Place of PublicationAmsterdam
PublisherElsevier
Chapter8
Pages149-173
Number of pages25
ISBN (Electronic)9780323675376
ISBN (Print)9780323675383
DOIs
Publication statusPublished - 5 Jun 2020

Keywords

  • Deep learning
  • Digital pathology
  • Image analysis
  • Machine learning
  • Precision pathology

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