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Monitoring Acute Lymphoblastic Leukemia Therapy with Stacked Denoising Autoencoders

  • Jakob Scheithe
  • Roxane LicandroEmail author
  • Paolo Rota
  • Michael Reiter
  • Markus Diem
  • Martin Kampel
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 31)

Abstract

For acute lymphoblastic leukemia treatment monitoring, the ratio of cancerous blood cells, called Minimal Residual Disease (MRD), is in practice assessed manually by experts. Using flow cytometry, single cells are classified as cancerous or healthy, based on a number of measured parameters. This task allows application of machine learning techniques, such as Stacked Denoising Autoencoders (DSAE). Seven different models’ performance in assessing MRD was evaluated. Higher model complexity does not guarantee better performance. For all models, a high number of false positives biases the predicted MRD value. Therefore, cost-sensitive learning is proposed as a way of improving classification performance.

Notes

Acknowledgements

This work was supported by the European Commission FP7-PEOPLE-2013-IAPP 610872 and by ZIT Life Sciences 2014 (1207843). We want to thank Michael Dworzak and Angela Schumich at the Children Cancer Research Center in Vienna for annotating and providing the FCM data.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jakob Scheithe
    • 1
  • Roxane Licandro
    • 1
    • 2
    Email author
  • Paolo Rota
    • 3
  • Michael Reiter
    • 1
  • Markus Diem
    • 1
  • Martin Kampel
    • 1
  1. 1.Computer Vision LabInstitute of Visual Computing and Human-Centered Technology, TU WienViennaAustria
  2. 2.Computational Imaging Research Lab, Department Biomedical Imaging and Image-guided TherapyMedical University of ViennaViennaAustria
  3. 3.Central Research Lab, Pattern Analysis and Computer VisionIstituto Italiano di TecnologiaGenovaItaly

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