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Deep Learning from Label Proportions for Emphysema Quantification

  • Gerda BortsovaEmail author
  • Florian Dubost
  • Silas Ørting
  • Ioannis Katramados
  • Laurens Hogeweg
  • Laura Thomsen
  • Mathilde Wille
  • Marleen de Bruijne
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)

Abstract

We propose an end-to-end deep learning method that learns to estimate emphysema extent from proportions of the diseased tissue. These proportions were visually estimated by experts using a standard grading system, in which grades correspond to intervals (label example: 1–5% of diseased tissue). The proposed architecture encodes the knowledge that the labels represent a volumetric proportion. A custom loss is designed to learn with intervals. Thus, during training, our network learns to segment the diseased tissue such that its proportions fit the ground truth intervals. Our architecture and loss combined improve the performance substantially (8% ICC) compared to a more conventional regression network. We outperform traditional lung densitometry and two recently published methods for emphysema quantification by a large margin (at least 7% AUC and 15% ICC), and achieve near-human-level performance. Moreover, our method generates emphysema segmentations that predict the spatial distribution of emphysema at human level.

Keywords

Emphysema quantification Weak labels Multiple instance learning Learning from label proportions 

Notes

Acknowledgment

This research is financed by the Netherlands Organization for Scientific Research (NWO) and COSMONiO.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Gerda Bortsova
    • 1
    Email author
  • Florian Dubost
    • 1
  • Silas Ørting
    • 2
  • Ioannis Katramados
    • 3
  • Laurens Hogeweg
    • 3
  • Laura Thomsen
    • 4
  • Mathilde Wille
    • 5
  • Marleen de Bruijne
    • 1
    • 2
  1. 1.Biomedical Imaging Group RotterdamErasmus MCRotterdamThe Netherlands
  2. 2.Department of Computer ScienceUniversity of CopenhagenCopenhagenDenmark
  3. 3.COSMONiOGroningenThe Netherlands
  4. 4.Department of Respiratory MedicineHvidovre HospitalHvidovreDenmark
  5. 5.Department of Diagnostic ImagingBispebjerg HospitalCopenhagenDenmark

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