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Authors: Paolo Rota 1 ; Florian Kleber 1 ; Michael Reiter 1 ; Stefanie Groeneveld-Krentz 2 and Martin Kampel 1

Affiliations: 1 TU Wien, Austria ; 2 Charité - Universitaetsmedizin, Germany

ISBN: 978-989-758-175-5

Keyword(s): Flow Cytometry, Leukemia (ALL), Deep Learning, Stacked Auto Encoders, GMM.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Computer Vision, Visualization and Computer Graphics ; Medical Image Applications

Abstract: In last years automated medical data analysis turned out to be one of the frontiers of Machine Learning. Medical operators are still reluctant to rely completely in automated solutions at diagnosis stage. However, Machine Learning researchers have focused their attention in this field, proposing valuable methods having often an outcome comparable to human evaluation. In this paper we give a brief overview on the role of Computer Vision and Machine Learning in solving medical problems in an automatic (supervised or unsupervised) fashion, we consider then a case study of Flow Cytometry data analysis for MRD assessment in Acute Lymphoblastic Leukemia. The clinical evaluation procedure of this type of data consists in a time taking manual labeling that can be performed only after an intensive training, however sometimes different experience may lead to different opinions. We are therefore proposing two different approaches: the first is generative semi-supervised Gaussian Mixture Model ba sed approach, the latter is a discriminative semi-supervised Deep Learning based approach. (More)

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Paper citation in several formats:
Rota P., Kleber F., Reiter M., Groeneveld-Krentz S. and Kampel M. (2016). The Role of Machine Learning in Medical Data Analysis. A Case Study: Flow Cytometry.In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 303-310. DOI: 10.5220/0005675903030310

@conference{visapp16,
author={Paolo Rota and Florian Kleber and Michael Reiter and Stefanie Groeneveld-Krentz and Martin Kampel},
title={The Role of Machine Learning in Medical Data Analysis. A Case Study: Flow Cytometry},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={303-310},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005675903030310},
isbn={978-989-758-175-5},
}

TY - CONF

JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)
TI - The Role of Machine Learning in Medical Data Analysis. A Case Study: Flow Cytometry
SN - 978-989-758-175-5
AU - Rota P.
AU - Kleber F.
AU - Reiter M.
AU - Groeneveld-Krentz S.
AU - Kampel M.
PY - 2016
SP - 303
EP - 310
DO - 10.5220/0005675903030310

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