Overview of the medical tasks in ImageCLEF 2016

García Seco de Herrera, Alba (Lister Hill National Center for Biomedical Communications, National Library of Medicine,Bethesda, USA) ; Schaer, Roger (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Bromuri, Stefano (Open University of the Netherlands, The Netherlands) ; Müller, Henning (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis))

ImageCLEF is the image retrieval task of the Conference and Labs of the Evaluation Forum (CLEF). ImageCLEF has historically focused on the multimodal and language{independent retrieval of images. Many tasks are related to image classication and the annotation of image data as well. The medical task has focused more on image retrieval in the beginning and then retrieval and classication tasks in subsequent years. In 2016 a main focus was the creation of meta data for a collection of medical images taken from articles of the the biomedical scientic literature. In total 8 teams participated in the four tasks and 69 runs were submitted. No team participated in the label prediction task, a totally new task. Deep learning has now been used for several of the ImageCLEF tasks and by many of the participants obtaining very good results. A majority of runs was submitting using deep learning and this follows general trends in machine learning. In several of the tasks multimodal approaches clearly led to best results.

Type de conférence:
full paper
Economie et Services
Institut Informatique de gestion
Adresse bibliogr.:
Evora, Portugal, 5-8 September 2016
Evora, Portugal
5-8 September 2016
13 p.
Publié dans
Proceedings of the 7th International Conference of CLEF Association (CLEF) 2016
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 Notice créée le 2016-09-28, modifiée le 2019-06-11

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