DEEP LEARNING APPROACHES FOR SMALL DIMENSIONAL BIOMEDICAL DATA

Karin Pröll

Publikation: Beitrag in Buch/Bericht/TagungsbandKonferenzbeitragBegutachtung

Abstract

In this paper we apply convolutional neuronal networks in different configurations to solve prediction tasks on medical data: Given 27 blood parameters obtained by labor blood examination the classes of tumor markers C153 and PSA should be predicted. Based on former work the results of trained Multi-Layer-Perceptrons (MLP) were moderate. Our major interest was now focused on the question if the prediction quality of CNN models outperforms MLPs. We had to transform the vector of input data into a two-dimensional pseudo image and augment it with different correlation values for increasing spatial structure. Various experiments with CNNs show that the prediction quality slightly increases compared to MLPs.
OriginalspracheEnglisch
Titel29th European Modeling and Simulation Symposium, EMSS 2017, Held at the International Multidisciplinary Modeling and Simulation Multiconference, I3M 2017
Redakteure/-innenFrancesco Longo, Michael Affenzeller, Miquel Angel Piera, Agostino G. Bruzzone, Emilio Jimenez
Seiten176-180
Seitenumfang5
ISBN (elektronisch)9781510847651
PublikationsstatusVeröffentlicht - 2017
VeranstaltungThe 29th European Modeling & Simulation Symposium EMSS 2017 - Barcelona, Spanien
Dauer: 18 Sep. 201720 Sep. 2017
http://www.msc-les.org/conf/emss2017/

Publikationsreihe

Name29th European Modeling and Simulation Symposium, EMSS 2017, Held at the International Multidisciplinary Modeling and Simulation Multiconference, I3M 2017

Konferenz

KonferenzThe 29th European Modeling & Simulation Symposium EMSS 2017
Land/GebietSpanien
OrtBarcelona
Zeitraum18.09.201720.09.2017
Internetadresse

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