The Role of Machine Learning in Medical Data Analysis. A Case Study: Flow Cytometry

Paolo Rota, Florian Kleber, Michael Reiter, Stefanie Groeneveld-Krentz, Martin Kampel

2016

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 based approach, the latter is a discriminative semi-supervised Deep Learning based approach.

Paper Citation

in Harvard Style

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

in Bibtex Style

@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},
}

in EndNote Style

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