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Computational Pathology

  • Peter J. Schüffler
  • Qing Zhong
  • Peter J. Wild
  • Thomas J. FuchsEmail author
Chapter
  • 713 Downloads

Abstract

Computational pathology offers a comprehensive framework for advanced study design in a wide range of research questions, as well as for standardized pipeline development for fast and reproducible computer-assisted routine diagnostics. This new field emerges at the border of pathology and computer science and shows high potential to revolutionize established workflows in research and clinic, since not only computational models get faster and more efficient than before but also since an incredible amount of training data is being generated in modern hospitals which is mandatory for the training of informed and validated models.

We review the field of computational pathology and illustrate on two research examples how it will contribute to an accurate, objective, and reproducible study design comprising informed data acquisition, advanced pattern recognition, and transparent model validation.

Keywords

Convolutional Neural Network Clear Cell Renal Cell Carcinoma Memorial Sloan Kettering Cancer Center PTEN Deletion Nucleus Segmentation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgment

We thank Norbert Wey and Monika Bieri from University Hospital Zurich for their manifold support and their careful evaluation of the scanning statistics at the hospital. This chapter was partly funded through the NIH/NCI Cancer Center Support Grant P30 CA008748.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Peter J. Schüffler
    • 1
  • Qing Zhong
    • 2
  • Peter J. Wild
    • 2
  • Thomas J. Fuchs
    • 1
    Email author
  1. 1.Memorial Sloan Kettering Cancer CenterNew YorkUSA
  2. 2.Department of Pathology and Molecular PathologyUniversity Hospital ZurichZurichSwitzerland

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