000001719 001__ 1719
000001719 005__ 20181005170951.0
000001719 037__ $$aCONFERENCE
000001719 041__ $$aeng
000001719 245__ $$aOverview of the medical tasks in ImageCLEF 2016
000001719 260__ $$aEvora, Portugal$$b5-8 September 2016$$c2016
000001719 269__ $$a2016-09
000001719 300__ $$a13 p.
000001719 506__ $$ahidden
000001719 520__ $$9eng$$aImageCLEF is the image retrieval task of the Conference and Labs of the Evaluation Forum (CLEF). ImageCLEF has historically focused on the multimodal and language{independent retrieval of images. Many tasks are related to image classication and the annotation of image data as well. The medical task has focused more on image retrieval in the beginning and then retrieval and classication tasks in subsequent years. In 2016 a main focus was the creation of meta data for a collection of medical images taken from articles of the the biomedical scientic literature. In total 8 teams participated in the four tasks and 69 runs were submitted. No team participated in the label prediction task, a totally new task. Deep learning has now been used for several of the ImageCLEF tasks and by many of the participants obtaining very good results. A majority of runs was submitting using deep learning and this follows general trends in machine learning. In several of the tasks multimodal approaches clearly led to best results.
000001719 592__ $$aHEG VS HES-SO Valais-Wallis - Haute Ecole de Gestion & Tourisme
000001719 592__ $$bInstitut Informatique de gestion
000001719 592__ $$cEconomie et Services
000001719 65017 $$aInformatique
000001719 6531_ $$9eng$$aImageCLEFmed
000001719 6531_ $$9eng$$acompound figure detection
000001719 6531_ $$9eng$$amulti-label classification
000001719 6531_ $$9eng$$afigure separation
000001719 6531_ $$9eng$$amodality classification
000001719 6531_ $$9eng$$acaption detection
000001719 655_7 $$afull paper
000001719 700__ $$aGarcía Seco de Herrera, Alba$$uLister Hill National Center for Biomedical Communications, National Library of Medicine,Bethesda, USA
000001719 700__ $$aSchaer, Roger$$uUniversity of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)
000001719 700__ $$aBromuri, Stefano$$uOpen University of the Netherlands, The Netherlands
000001719 700__ $$aMüller, Henning$$uUniversity of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)
000001719 711__ $$aInternational Conference and Labs of the Evaluation Forum CLEF (2016)$$cEvora, Portugal$$d05/09/2016 / 07/09/2016
000001719 773__ $$tProceedings of the 7th International Conference of CLEF Association (CLEF) 2016
000001719 8564_ $$s429670$$uhttp://hesso.tind.io/record/1719/files/schaer_overviewmedicaltasks_2016.pdf
000001719 909CO $$ooai:hesso.tind.io:1719$$pDoc_type_Conferences$$pGLOBAL_SET$$qDoc_type_Rapport$$qDoc_type_Articles$$qDoc_type_Livres$$qDoc_type_Theses$$qDoc_type_Media$$qDoc_type_Chapitre_livre$$qDoc_type_Preprint$$qHEG-GE:public_conference
000001719 906__ $$aNONE
000001719 950__ $$aI1
000001719 980__ $$aconference