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.


Mots-clés:
Type de conférence:
full paper
Ecole:
HES-SO Valais-Wallis
Institut:
Institut Informatique de gestion
Classification:
Informatique
Adresse bibliogr.:
Evora, Portugal, 5-8 September 2016
Date:
Evora, Portugal
5-8 September 2016
2016
Pagination:
13 p.
Titre du document hôte:
Proceedings of the 7th International Conference of CLEF Association (CLEF) 2016
Le document apparaît dans:

Note  Le statut de ce document est: non diffusé

Note: The status of this file is: restricted


 Notice créée le 2016-09-28, modifiée le 2017-09-09

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