|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|
|Project(s)||Efficient EONS: Execution of Large Workloads on Elastic Heterogeneous Resources|
|Publication Type||Proceedings, refereed|
|Year of Publication||2018|
|Conference Name||Working Notes Proceedings of the MediaEval 2018 Workshop|
|Publisher||CEUR Workshop Proceedings|
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.