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Multiple Instance Learning for Heterogeneous Images: Training a CNN for Histopathology

  • Heather D. CoutureEmail author
  • J. S. Marron
  • Charles M. Perou
  • Melissa A. Troester
  • Marc Niethammer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)

Abstract

Multiple instance (MI) learning with a convolutional neural network enables end-to-end training in the presence of weak image-level labels. We propose a new method for aggregating predictions from smaller regions of the image into an image-level classification by using the quantile function. The quantile function provides a more complete description of the heterogeneity within each image, improving image-level classification. We also adapt image augmentation to the MI framework by randomly selecting cropped regions on which to apply MI aggregation during each epoch of training. This provides a mechanism to study the importance of MI learning. We validate our method on five different classification tasks for breast tumor histology and provide a visualization method for interpreting local image classifications that could lead to future insights into tumor heterogeneity.

Notes

Acknowledgments

This work was supported by a grant from the UNC Lineberger Comprehensive Cancer Center funded by the University Cancer Research Fund (LCCC2017T204), NCI Breast SPORE program (P50-CA58223), and the Breast Cancer Research Foundation.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Heather D. Couture
    • 1
    Email author
  • J. S. Marron
    • 2
    • 3
  • Charles M. Perou
    • 2
    • 4
  • Melissa A. Troester
    • 2
    • 5
  • Marc Niethammer
    • 1
    • 6
  1. 1.Department of Computer ScienceUniversity of North CarolinaChapel HillUSA
  2. 2.Lineberger Comprehensive Cancer CenterUniversity of North CarolinaChapel HillUSA
  3. 3.Department of Statistics and Operations ResearchUniversity of North CarolinaChapel HillUSA
  4. 4.Department of GeneticsUniversity of North CarolinaChapel HillUSA
  5. 5.Department of EpidemiologyUniversity of North CarolinaChapel HillUSA
  6. 6.Biomedical Research Imaging CenterUniversity of North CarolinaChapel HillUSA

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