Transfer learning with prioritized classification and training dataset equalization for medical objects detection

Authors	O. Ostroukhova, K. Pogorelov, M. Riegler, D. Dang-Nguyen and P. Halvorsen
Title	Transfer learning with prioritized classification and training dataset equalization for medical objects detection
Afilliation	Communication Systems
Project(s)	Efficient EONS: Execution of Large Workloads on Elastic Heterogeneous Resources, Department of Holistic Systems
Status	Published
Publication Type	Proceedings, refereed
Year of Publication	2018
Conference Name	Working Notes Proceedings of the MediaEval 2018 Workshop
Publisher	CEUR Workshop Proceedings
Abstract	This paper presents the method proposed by the organizer team (SIMULA) for MediaEval 2018 Multimedia for Medicine: the Medico Task. We utilized the recent transfer-learning-based image classification methodology and focused on how easy it is to implement multi-class image classifiers in general and how to improve the classification performance without deep neural network model redesign. The goal for this was both to provide a baseline for the Medico task and to show the performance of out-of-the-box classifiers for the medical use-case scenario.
Citation Key	26258

Search form