Unsupervised online clustering and detection algorithms using crowdsourced data for malaria diagnosis

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RECERCAT Home > Universitat Politècnica de Catalunya > Documents de recerca > View document

To access the full text documents, please follow this link: http://hdl.handle.net/2117/124243

dc.contributor	Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.contributor	Universitat Politècnica de Catalunya. SPCOM - Grup de Recerca de Processament del Senyal i Comunicacions
dc.contributor.author	Pagès Zamora, Alba Maria
dc.contributor.author	Cabrera-Bean, Margarita
dc.contributor.author	Diaz Vilor, Carles
dc.date	2019-02-01
dc.identifier.citation	Pagès-Zamora, A., Cabrera, M., Diaz, C. Unsupervised online clustering and detection algorithms using crowdsourced data for malaria diagnosis. "Pattern recognition", 1 Febrer 2019, vol. 86, p. 209-223.
dc.identifier.citation	0031-3203
dc.identifier.citation	10.1016/j.patcog.2018.09.001
dc.identifier.uri	http://hdl.handle.net/2117/124243
dc.language.iso	eng
dc.relation	https://www.sciencedirect.com/science/article/abs/pii/S0031320318303200
dc.rights	Attribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject	Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Reconeixement de formes
dc.subject	Pattern recognition systems
dc.subject	Contracting out -- Technological innovations
dc.subject	Crowdsourcing
dc.subject	Unreliable annotators
dc.subject	Unsupervised method
dc.subject	Online EM algorithm
dc.subject	MalariaSpot
dc.subject	Reconeixement de formes (Informàtica)
dc.subject	Contractació externa -- Innovacions tecnològiques
dc.title	Unsupervised online clustering and detection algorithms using crowdsourced data for malaria diagnosis
dc.type	info:eu-repo/semantics/draft
dc.type	info:eu-repo/semantics/article
dc.description.abstract	© <2018>. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.description.abstract	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.
dc.description.abstract	Peer Reviewed

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