Machine Learning and Knowledge Extraction in Digital Pathology Needs an Integrative Approach

  • Andreas Holzinger
  • Bernd Malle
  • Peter Kieseberg
  • Peter M. Roth
  • Heimo Müller
  • Robert Reihs
  • Kurt Zatloukal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10344)

Abstract

During the last decade pathology has benefited from the rapid progress of image digitizing technologies, which led to the development of scanners, capable to produce so-called Whole Slide images (WSI) which can be explored by a pathologist on a computer screen comparable to the conventional microscope and can be used for diagnostics, research, archiving and also education and training. Digital pathology is not just the transformation of the classical microscopic analysis of histological slides by pathologists to just a digital visualization. It is a disruptive innovation that will dramatically change medical work-flows in the coming years and help to foster personalized medicine. Really powerful gets a pathologist if she/he is augmented by machine learning, e.g. by support vector machines, random forests and deep learning. The ultimate benefit of digital pathology is to enable to learn, to extract knowledge and to make predictions from a combination of heterogenous data, i.e. the histological image, the patient history and the *omics data. These challenges call for integrated/integrative machine learning approach fostering transparency, trust, acceptance and the ability to explain step-by-step why a decision has been made.

Keywords

Digital pathology Data integration Integrative machine learning Deep learning Transfer learning 

Notes

Acknowledgments

We are grateful for valuable comments from the international reviewers.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Andreas Holzinger
    • 1
  • Bernd Malle
    • 1
    • 2
  • Peter Kieseberg
    • 1
    • 2
  • Peter M. Roth
    • 3
  • Heimo Müller
    • 1
    • 4
  • Robert Reihs
    • 1
    • 4
  • Kurt Zatloukal
    • 4
  1. 1.Holzinger Group, HCI-KDD, Institute for Medical Informatics/StatisticsMedical University GrazGrazAustria
  2. 2.SBA ResearchViennaAustria
  3. 3.Institute of Computer Graphics and VisionGraz University of TechnologyGrazAustria
  4. 4.Institute of PathologyMedical University GrazGrazAustria

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