Counting Malaria parasites with a two-stage EM based algorithm using crowdsourced data
Document typeConference report
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessRestricted access - publisher's policy
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 lowcost 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.
CitationCabrera, M., Pages, A., Diaz, C., Postigo-Camps, M., Cuadrado-Sanchez, D., Luengo-Oroz, M. Counting Malaria parasites with a two-stage EM based algorithm using crowdsourced data. A: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. "EMBC'17: 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: July 11-15, 2017: International Convention Center (ICC), Jeju Island, Korea". Jeju Island, South Korea: Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 2283-2287.