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Proceedings Paper

A game-based platform for crowd-sourcing biomedical image diagnosis and standardized remote training and education of diagnosticians
Author(s): Steve Feng; Minjae Woo; Krithika Chandramouli; Aydogan Ozcan
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Paper Abstract

Over the past decade, crowd-sourcing complex image analysis tasks to a human crowd has emerged as an alternative to energy-inefficient and difficult-to-implement computational approaches. Following this trend, we have developed a mathematical framework for statistically combining human crowd-sourcing of biomedical image analysis and diagnosis through games. Using a web-based smart game (BioGames), we demonstrated this platform’s effectiveness for telediagnosis of malaria from microscopic images of individual red blood cells (RBCs). After public release in early 2012 (, more than 3000 gamers (experts and non-experts) used this BioGames platform to diagnose over 2800 distinct RBC images, marking them as positive (infected) or negative (non-infected). Furthermore, we asked expert diagnosticians to tag the same set of cells with labels of positive, negative, or questionable (insufficient information for a reliable diagnosis) and statistically combined their decisions to generate a gold standard malaria image library. Our framework utilized minimally trained gamers’ diagnoses to generate a set of statistical labels with an accuracy that is within 98% of our gold standard image library, demonstrating the “wisdom of the crowd”. Using the same image library, we have recently launched a web-based malaria training and educational game allowing diagnosticians to compare their performance with their peers. After diagnosing a set of ~500 cells per game, diagnosticians can compare their quantified scores against a leaderboard and view their misdiagnosed cells. Using this platform, we aim to expand our gold standard library with new RBC images and provide a quantified digital tool for measuring and improving diagnostician training globally.

Paper Details

Date Published: 12 March 2015
PDF: 8 pages
Proc. SPIE 9314, Optics and Biophotonics in Low-Resource Settings, 93140J (12 March 2015); doi: 10.1117/12.2077884
Show Author Affiliations
Steve Feng, Univ. of California, Los Angeles (United States)
Minjae Woo, Univ. of California, Los Angeles (United States)
Krithika Chandramouli, Univ. of California, Los Angeles (United States)
Aydogan Ozcan, Univ. of California, Los Angeles (United States)

Published in SPIE Proceedings Vol. 9314:
Optics and Biophotonics in Low-Resource Settings
David Levitz; Aydogan Ozcan; David Erickson, Editor(s)

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