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

Cabrera-Bean, Margarita; Pagès-Zamora, Alba; Diaz-Vilor, Carles; Postigo-Camps, Maria; Cuadrado-Sanchez, Daniel; Luengo-Oroz, Miguel

doi:10.1109/embc.2017.8037311

Cited by 4 publications

(11 citation statements)

References 10 publications

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“…Classify (Albarqouni et al, 2016a), (Brady et al, 2014), (Brady et al, 2017), , (dos Reis et al, 2015), (Eickhoff, 2014), (Foncubierta Rodríguez and Müller, 2012), (Gur et al, 2017), (de Herrera et al, 2014), (Holst et al, 2015), (Huang and Hamarneh, 2017), (Keshavan et al, 2018), (Lawson et al, 2017), (Malpani et al, 2015), (Mavandadi et al, 2012, (Mitry et al, 2013), (Mitry et al, 2015), (Nguyen et al, 2012), (Park et al, 2016), (Park et al, 2017), (Smittenaar et al, 2018), (Sonabend et al, 2017), (Sullivan et al, 2018) Segment (Roethlingshoefer et al, 2017), (Boorboor et al, 2018), (Bruggemann et al, 2018), (Cabrera-Bean et al, 2017), (Chávez-Aragón et al, 2013), (Cheplygina et al, 2016), (Ganz et al, 2017), (Gurari et al, 2015b), (Heller et al, 2017), (Irshad et al, 2015), (Lee and Tufail, 2014), (Lee et al, 2016), (Lejeune et al, 2017), (Luengo-Oroz et al, 2012), (Maier-Hein et al, 2014a), (Maier-Hein et al, 2016), (O'Neil et al, 2017), (Park et al, 2018),…”

Section: Task Papersmentioning

confidence: 99%

“…Abdomen (Roethlingshoefer et al, 2017), (Heim, 2018), (Heller et al, 2017), (Maier-Hein et al, 2014a), (Maier-Hein et al, 2014b), (Maier-Hein et al, 2015), (Maier-Hein et al, 2016), (McKenna et al, 2012), (Nguyen et al, 2012), (Park et al, 2016), (Park et al, 2017), (Park et al, 2018), (Rajchl et al, 2017) Brain (Ganz et al, 2017), (Keshavan et al, 2018), (Rajchl et al, 2016), (Sonabend et al, 2017), (Timmermans et al, 2016) Eye (Brady et al, 2014), (Brady et al, 2017), (Lee and Tufail, 2014), (Lee et al, 2016), (Leifman et al, 2015), (Mitry et al, 2013), (Mitry et al, 2015), (Mitry et al, 2016) Heart (Gur et al, 2017) Histo (Albarqouni et al, 2016a), (Albarqouni et al, 2016b), (Bruggemann et al, 2018), (Cabrera-Bean et al, 2017), (Della Mea et al, 2014), (dos Reis et al, 2015), (Eickhoff, 2014), (Irshad et al, 2015), (Irshad et al, 2017), (Lawson et al, 2017), (Luengo-Oroz et al, 2012), (Mavandadi et al, 2012), (Sameki et al, 2016), (Sharma et al, 2017), ...…”

Section: Domain Papersmentioning

confidence: 99%

“…Click (Bruggemann et al, 2018), (Cabrera-Bean et al, 2017), (Della Mea et al, 2014), (Huang and Hamarneh, 2017), (Lawson et al, 2017), (Lejeune et al, 2017), (Luengo-Oroz et al, 2012), (Park et al, 2018), (Rajchl et al, 2016) Click + Compare (Maier-Hein et al, 2014b), (Maier-Hein et al, 2015), (Maier-Hein et al, 2016) Click + Draw (Irshad et al, 2015) Compare (Ørting et al, 2017) Draw (Roethlingshoefer et al, 2017), (Boorboor et al, 2018), (Chávez-Aragón et al, 2013), (Cheplygina et al, 2016), (Ganz et al, 2017), (Gurari et al, 2015b), (Heller et al, 2017), (Lee and Tufail, 2014), (Lee et al, 2016), (Maier-Hein et al, 2014a), (O'Neil et al, 2017), (Sameki et al, 2016), (Sharma et al, 2017) Rate (Albarqouni et al, 2016a), (Albarqouni et al, 2016b), (Brady et al, 2014), (Brady et al, 2017), , (dos Reis et al, 2015), (Eickhoff, 2014), (Foncubierta Rodríguez and Müller, 2012), (Gur et al, 2017), (de Herrera et al, 2014), (Holst et al, 2015), (Keshavan et al, 2018), (Mavandadi et al, 2012), (McKenna et al, 2012), (Mitry ...…”

Section: Interaction Papersmentioning

confidence: 99%

“…Evaluating existing annotations is always done with rating (Foncubierta Rodríguez and Müller, 2012;de Herrera et al, 2014) or drawing (Roethlingshoefer et al, 2017;Ganz et al, 2017) interactions. Different types of annotations were often collected based on the type of task information being sought, e.g., clicking to obtain counts and locations of specific image features (Cabrera-Bean et al, 2017;Della Mea et al, 2014) and drawing to determine segmentation boundaries (Gurari et al, 2015b;Chávez-Aragón et al, 2013). Ratings were a more general type of interaction and could be used to classify for whole-image level discrimination of image feature presence or category (Keshavan et al, 2018;de Herrera et al, 2014;McKenna et al, 2012) or to obtain estimates of feature certainty (dos Reis et al, 2015).…”

Section: Interaction Papersmentioning

confidence: 99%

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A Survey of Crowdsourcing in Medical Image Analysis

Ørting

Doyle²,

Hilten

et al. 2020

HCj

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Rapid advances in image processing capabilities have been seen across many domains, fostered by the application of machine learning algorithms to "big-data". However, within the realm of medical image analysis, advances have been curtailed, in part, due to the limited availability of large-scale, well-annotated datasets. One of the main reasons for this is the high cost often associated with producing large amounts of high-quality meta-data. Recently, there has been growing interest in the application of crowdsourcing for this purpose; a technique that has proven effective for creating large-scale datasets across a range of disciplines, from computer vision to astrophysics. Despite the growing popularity of this approach, there has not yet been a comprehensive literature review to provide guidance to researchers considering using crowdsourcing methodologies in their own medical imaging analysis. In this survey, we review studies applying crowdsourcing to the analysis of medical images, published prior to July 2018. We identify common approaches, challenges and considerations, providing guidance of utility to researchers adopting this approach. Finally, we discuss future opportunities for development within this emerging domain.

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Section: Task Papersmentioning

confidence: 99%

Section: Domain Papersmentioning

confidence: 99%

Section: Interaction Papersmentioning

confidence: 99%

Section: Interaction Papersmentioning

confidence: 99%

See 2 more Smart Citations

A Survey of Crowdsourcing in Medical Image Analysis

Ørting

Doyle²,

Hilten

et al. 2020

HCj

View full text Add to dashboard Cite

show abstract

“…The full batch procedure was partially published in[24,25] without the harmonized notation and with very limited experimental results. A preliminary simpler version of the online implementation of the detection stage with a fixed set of parameters was also included in[25].…”

mentioning

confidence: 99%

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

Pagès-Zamora

Cabrera-Bean

Diaz-Vilor

2019

Pattern Recognition

Self Cite

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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.

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Untitled

2020

HCj

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

Cited by 4 publications

References 10 publications

A Survey of Crowdsourcing in Medical Image Analysis

A Survey of Crowdsourcing in Medical Image Analysis

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

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