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Brain Tissue Entropy Changes in Patients with Autism Spectrum Disorder Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Monitoring Acute Lymphoblastic Leukemia Therapy with Stacked Denoising Autoencoders

Authors : Jakob Scheithe, Roxane Licandro, Paolo Rota, Michael Reiter, Markus Diem, Martin Kampel

Published in: Computer Aided Intervention and Diagnostics in Clinical and Medical Images

Publisher: Springer International Publishing

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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.

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Footnotes
1
For the conducted randomized clinical trial AIEOP-BFM 2009, approximately 2000 ALL patients between age 1–18 years in 20 countries in and outside Europe were observed per year (see http://​www.​bfm-international.​org/​trials.​php [assessed 2018-04-14]).
 
2
See [19, 8.5.1] for a definition of the performance metrics.
 
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Metadata
Title
Monitoring Acute Lymphoblastic Leukemia Therapy with Stacked Denoising Autoencoders
Authors
Jakob Scheithe
Roxane Licandro
Paolo Rota
Michael Reiter
Markus Diem
Martin Kampel
Copyright Year
2019
Book
Computer Aided Intervention and Diagnostics in Clinical and Medical Images

Print ISBN: 978-3-030-04060-4

Electronic ISBN: 978-3-030-04061-1

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-04061-1_19