Counting malaria parasites with a two-stage EM based algorithm using crowsourced data

Cabrera Bean, Margarita and Pagés Zamora, Alba and Díaz Vilor, Carles and Postigo Camps, María and Cuadrado Sánchez, Daniel and Luengo Oroz, Miguel Ángel (2017). Counting malaria parasites with a two-stage EM based algorithm using crowsourced data. In: "39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2017)", 11/07/2017 - 15/07/2017, Seogwipo, South Korea. pp. 2283-2287. https://doi.org/10.1109/EMBC.2017.8037311.

Description

Title: Counting malaria parasites with a two-stage EM based algorithm using crowsourced data
Author/s:
  • Cabrera Bean, Margarita
  • Pagés Zamora, Alba
  • Díaz Vilor, Carles
  • Postigo Camps, María
  • Cuadrado Sánchez, Daniel
  • Luengo Oroz, Miguel Ángel
Item Type: Presentation at Congress or Conference (Article)
Event Title: 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2017)
Event Dates: 11/07/2017 - 15/07/2017
Event Location: Seogwipo, South Korea
Title of Book: 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2017)
Título de Revista/Publicación: 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2017)
Date: 2017
ISSN: 1094-687X
Subjects:
Freetext Keywords: Crowdsourcing, Malaria thick smear, EM algorithm, robust clustering
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Ingeniería Electrónica
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Abstract: Malaria eradication of the worldwide is currently one of the main WHO's global goals. In this work, we focus on the use of human-machine interaction strategies for low-cost fast reliable malaria diagnostic based on a crowdsourced approach. The addressed technical problem consists in detecting spots in images even under very harsh conditions when positive objects are very similar to some artifacts. The clicks or tags delivered by several annotators labeling an image are modeled as a robust finite mixture, and techniques based on the Expectation-Maximization (EM) algorithm are proposed for accurately counting malaria parasites on thick blood smears obtained by microscopic Giemsa-stained techniques. This approach outperforms other traditional methods as it is shown through experimentation with real data.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainTEC2013-41315-RUnspecifiedUnspecifiedUnspecified
Government of SpainTEC2016-77148-C2-1-RUnspecifiedUnspecifiedUnspecified
Government of SpainTEC2016-75067-C4-2-RUnspecifiedUnspecifiedUnspecified
Government of SpainTEC2015-69648-REDCUnspecifiedUnspecifiedUnspecified

More information

Item ID: 50720
DC Identifier: http://oa.upm.es/50720/
OAI Identifier: oai:oa.upm.es:50720
DOI: 10.1109/EMBC.2017.8037311
Official URL: https://ieeexplore.ieee.org/abstract/document/8037311/
Deposited by: Memoria Investigacion
Deposited on: 28 May 2018 17:05
Last Modified: 28 May 2018 17:05
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