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Entropy 2014, 16(7), 3866-3877; doi:10.3390/e16073866
Article

Many Can Work Better than the Best: Diagnosing with Medical Images via Crowdsourcing

1
, 2,3
, 1
, 4
, 1,*  and 3
1 Department of Interventional Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510080, China 2 School of Economics and Commerce, South China University of Technology, Guangzhou 510006, China 3 Software Institute, Sun Yat-Sen University, Guangzhou 510275, China 4 Department of Gynaecology and Obstetrics, Guangzhou Women and Children Medical Center, Guangzhou 510623, China
* Author to whom correspondence should be addressed.
Received: 8 March 2014 / Revised: 22 June 2014 / Accepted: 3 July 2014 / Published: 14 July 2014
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Abstract

We study a crowdsourcing-based diagnosis algorithm, which is against the fact that currently we do not lack medical staff, but high level experts. Our approach is to make use of the general practitioners’ efforts: For every patient whose illness cannot be judged definitely, we arrange for them to be diagnosed multiple times by different doctors, and we collect the all diagnosis results to derive the final judgement. Our inference model is based on the statistical consistency of the diagnosis data. To evaluate the proposed model, we conduct experiments on both the synthetic and real data; the results show that it outperforms the benchmarks.
Keywords: medical images based diagnosis; crowdsourcing; entropy; Kullback–Leibler divergence medical images based diagnosis; crowdsourcing; entropy; Kullback–Leibler divergence
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Xiang, X.-H.; Huang, X.-Y.; Zhang, X.-L.; Cai, C.-F.; Yang, J.-Y.; Li, L. Many Can Work Better than the Best: Diagnosing with Medical Images via Crowdsourcing. Entropy 2014, 16, 3866-3877.

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