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

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Book cover Towards Integrative Machine Learning and Knowledge Extraction

Part of the book series: Lecture Notes in Computer Science ((LNAI,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.

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Notes

  1. 1.

    Regulation (EU) 2016/679 of the European Parliament and of the council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing directive 95/46/EC (General Data Protection Regulation).

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Acknowledgments

We are grateful for valuable comments from the international reviewers.

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Authors and Affiliations

  1. Holzinger Group, HCI-KDD, Institute for Medical Informatics/Statistics, Medical University Graz, Graz, Austria

    Andreas Holzinger, Bernd Malle, Peter Kieseberg, Heimo Müller & Robert Reihs

  2. SBA Research, Vienna, Austria

    Bernd Malle & Peter Kieseberg

  3. Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria

    Peter M. Roth

  4. Institute of Pathology, Medical University Graz, Graz, Austria

    Heimo Müller, Robert Reihs & Kurt Zatloukal

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  1. Andreas Holzinger
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  2. Bernd Malle
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  7. Kurt Zatloukal
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Correspondence to Andreas Holzinger .

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  1. Medical University Graz, Graz, Austria

    Andreas Holzinger

  2. University of Alberta, Edmonton, Alberta, Canada

    Randy Goebel

  3. Bologna University, Bologna, Italy

    Massimo Ferri

  4. Coventry University, Coventry, United Kingdom

    Vasile Palade

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Holzinger, A. et al. (2017). Machine Learning and Knowledge Extraction in Digital Pathology Needs an Integrative Approach. In: Holzinger, A., Goebel, R., Ferri, M., Palade, V. (eds) Towards Integrative Machine Learning and Knowledge Extraction. Lecture Notes in Computer Science(), vol 10344. Springer, Cham. https://doi.org/10.1007/978-3-319-69775-8_2

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  • DOI: https://doi.org/10.1007/978-3-319-69775-8_2

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