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 |