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research
Unsupervised online clustering and detection algorithms using crowdsourced data for malaria diagnosis
Authors
Margarita Cabrera-Bean
Carles Diaz Vilor
Alba Maria Pagès Zamora
Publication date
1 January 2019
Publisher
'Elsevier BV'
Doi
Cite
Abstract
© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Crowdsourced data in science might be severely error-prone due to the inexperience of annotators participating in the project. In this work, we present a procedure to detect specific structures in an image given tags provided by multiple annotators and collected through a crowdsourcing methodology. The procedure consists of two stages based on the Expectation–Maximization (EM) algorithm, one for clustering and the other one for detection, and it gracefully combines data coming from annotators with unknown reliability in an unsupervised manner. An online implementation of the approach is also presented that is well suited to crowdsourced streaming data. Comprehensive experimental results with real data from the MalariaSpot project are also included.Peer ReviewedPreprin
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UPCommons. Portal del coneixement obert de la UPC
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oai:upcommons.upc.edu:2117/124...
Last time updated on 02/02/2019
UPCommons
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:upcommons.upc.edu:2117/124...
Last time updated on 17/04/2020