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Proceedings Paper

A game-based crowdsourcing platform for rapidly training middle and high school students to perform biomedical image analysis
Author(s): Steve Feng; Min-jae Woo; Hannah Kim; Eunso Kim; Sojung Ki; Lei Shao; Aydogan Ozcan
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Paper Abstract

We developed an easy-to-use and widely accessible crowd-sourcing tool for rapidly training humans to perform biomedical image diagnostic tasks and demonstrated this platform’s ability on middle and high school students in South Korea to diagnose malaria infected red-blood-cells (RBCs) using Giemsa-stained thin blood smears imaged under light microscopes. We previously used the same platform (i.e., BioGames) to crowd-source diagnostics of individual RBC images, marking them as malaria positive (infected), negative (uninfected), or questionable (insufficient information for a reliable diagnosis). Using a custom-developed statistical framework, we combined the diagnoses from both expert diagnosticians and the minimally trained human crowd to generate a gold standard library of malaria-infection labels for RBCs. Using this library of labels, we developed a web-based training and educational toolset that provides a quantified score for diagnosticians/users to compare their performance against their peers and view misdiagnosed cells. We have since demonstrated the ability of this platform to quickly train humans without prior training to reach high diagnostic accuracy as compared to expert diagnosticians. Our initial trial group of 55 middle and high school students has collectively played more than 170 hours, each demonstrating significant improvements after only 3 hours of training games, with diagnostic scores that match expert diagnosticians’. Next, through a national-scale educational outreach program in South Korea we recruited >1660 students who demonstrated a similar performance level after 5 hours of training. We plan to further demonstrate this tool’s effectiveness for other diagnostic tasks involving image labeling and aim to provide an easily-accessible and quickly adaptable framework for online training of new diagnosticians.

Paper Details

Date Published: 11 March 2016
PDF: 9 pages
Proc. SPIE 9699, Optics and Biophotonics in Low-Resource Settings II, 96990T (11 March 2016); doi: 10.1117/12.2212310
Show Author Affiliations
Steve Feng, Univ. of California, Los Angeles (United States)
Min-jae Woo, Univ. of California, Los Angeles (United States)
Hannah Kim, Univ. of California, Los Angeles (United States)
Eunso Kim, Univ. of California, Los Angeles (United States)
Sojung Ki, Univ. of California, Los Angeles (United States)
Lei Shao, Univ. of California, Los Angeles (United States)
Aydogan Ozcan, Univ. of California, Los Angeles (United States)


Published in SPIE Proceedings Vol. 9699:
Optics and Biophotonics in Low-Resource Settings II
David Levitz; Aydogan Ozcan; David Erickson, Editor(s)

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