Wright, AI, Magee, DR, Quirke, P et al. (1 more author) (2015) Prospector: A web-based tool for rapid acquisition of gold standard data for pathology research and image analysis. Journal of Pathology Informatics, 6. 21 - 21. ISSN 2229-5089
Abstract
BACKGROUND: Obtaining ground truth for pathological images is essential for various experiments, especially for training and testing image analysis algorithms. However, obtaining pathologist input is often difficult, time consuming and expensive. This leads to algorithms being over-fitted to small datasets, and inappropriate validation, which causes poor performance on real world data. There is a great need to gather data from pathologists in a simple and efficient manner, in order to maximise the amount of data obtained. METHODS: We present a lightweight, web-based HTML5 system for administering and participating in data collection experiments. The system is designed for rapid input with minimal effort, and can be accessed from anywhere in the world with a reliable internet connection. RESULTS: We present two case studies that use the system to assess how limitations on fields of view affect pathologist agreement, and to what extent poorly stained slides affect judgement. In both cases, the system collects pathologist scores at a rate of less than two seconds per image. CONCLUSIONS: The system has multiple potential applications in pathology and other domains.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2015 Wright AI. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
Keywords: | Data acquisition; gold standard; ground truth; training data; web experiment system |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Artificial Intelligence & Biological Systems (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 01 Oct 2015 13:48 |
Last Modified: | 02 Oct 2015 13:50 |
Published Version: | http://dx.doi.org/10.4103/2153-3539.157785 |
Status: | Published |
Publisher: | Medknow Publications |
Identification Number: | 10.4103/2153-3539.157785 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:89184 |