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