000002139 001__ 2139
000002139 005__ 20190211171337.0
000002139 037__ $$aCONFERENCE
000002139 041__ $$aeng
000002139 245__ $$aUsing crowdsourcing for multi-label biomedical compound figure annotation
000002139 260__ $$aAthens, Greece$$b17-21 October 2016$$c2016
000002139 269__ $$a2016-10
000002139 300__ $$a10 p.
000002139 506__ $$avisible
000002139 520__ $$9eng$$aAbstract. Information analysis or retrieval for images in the biomedical literature needs to deal with a large amount of compound figures (figures containing several subfigures), as they constitute probably more than half of all images in repositories such as PubMed Central, which was the data set used for the task. The ImageCLEFmed benchmark proposed among other tasks in 2015 and 2016 a multi–label classification task, which aims at evaluating the automatic classification of figures into 30 image types. This task was based on compound figures and thus the figures were distributed to participants as compound figures but also in a sep-arated form. Therefore, the generation of a gold standard was required, so that algorithms of participants can be evaluated and compared. This work presents the process carried out to generate the multi–labels of ∼ 2650 compound figures using a crowdsourcing approach. Automatic algorithms to separate compound figures into subfigures were used and the results were then validated or corrected via crowdsourcing. The im-age types (MR, CT, X–ray, ...) were also annotated by crowdsourcing including detailed quality control. Quality control is necessary to insure quality of the annotated data as much as possible. ∼ 625 hours were invested with a cost of ∼ 870$.
000002139 546__ $$aEnglish
000002139 540__ $$acorrect
000002139 592__ $$aHEG-VS
000002139 592__ $$bInstitut Informatique de gestion
000002139 592__ $$cEconomie et Services
000002139 65017 $$aEconomie/gestion
000002139 6531_ $$9eng$$amulti–label annotation
000002139 6531_ $$9eng$$acompound figures
000002139 6531_ $$9eng$$acrowdsourcing
000002139 655_7 $$afull paper
000002139 700__ $$aGarcía Seco de Herrera, Alba$$uLister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, USA
000002139 700__ $$aSchaer, Roger$$uUniversity of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)
000002139 700__ $$aAntani, Sameer$$uLister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, USA
000002139 700__ $$aMüller, Henning$$uUniversity of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)
000002139 711__ $$a19th International Conference on medical image computing & Computer Assisted Intervention (MICCAI 2016)$$cAthens, Greece$$d17/10/2016 / 21/10/2016
000002139 773__ $$tProceedings of the 19th International Conference on medical image computing & Computer Assisted Intervention (MICCAI 2016)
000002139 8564_ $$s787461$$uhttps://hesso.tind.io/record/2139/files/Schaer_2017_using_crowdsourcing.pdf
000002139 8564_ $$s2190648$$uhttps://hesso.tind.io/record/2139/files/Schaer_2017_using_crowdsourcing.pdf?subformat=pdfa$$xpdfa
000002139 909CO $$ooai:hesso.tind.io:2139$$pGLOBAL_SET
000002139 906__ $$aGREEN
000002139 950__ $$aI1
000002139 980__ $$aconference