Authors | S. Hicks, A. Petlund, T. de Lange, P. T. Schmidt, P. Halvorsen, M. Riegler, P. H. Smedsrud, T. B. Haugen, K. R. Randel, K. Pogorelov et al. |
Editors | M. Larson, G. Gravier, H. Hung, C. Ngo and W. T. Ooi |
Title | ACM Multimedia BioMedia 2019 Grand Challenge Overview |
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Status | Published |
Publication Type | Proceedings, refereed |
Year of Publication | 2019 |
Conference Name | The ACM International Conference on Multimedia (ACM MM) |
Pagination | 2563-2567 |
Date Published | 10/2019 |
Publisher | ACM Press |
Place Published | New York, New York, USA |
ISBN Number | 9781450368896 |
Abstract | The BioMedia 2019 ACM Multimedia Grand Challenge is the first in a series of competitions focusing on the use of multimedia for different medical use-cases. In this year’s challenge, the participants are asked to develop efficient algorithms which automatically detect a variety of findings commonly identified in the gastrointestinal (GI) tract (a part of the human digestive system). The purpose of this task is to develop methods to aid medical doctors performing routine endoscopy inspections of the GI tract. In this paper, we give a detailed description of the four different tasks of this year’s challenge, present the datasets used for training and testing, and discuss how each submission is evaluated both qualitatively and quantitatively. |
URL | http://dl.acm.org/citation.cfm?doid=3343031http://dl.acm.org/citation.cfm?doid=3343031.3356058http://dl.acm.org/ft_gateway.cfm?id=3356058&ftid=2092095&dwn=1 |
DOI | 10.1145/334303110.1145/3343031.3356058 |
Citation Key | 26858 |