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Crowdsourcing for Medical Image Classification

García Seco de Herrera, Alba and Foncubierta-Rodríguez, Antonio and Markonis, Dimitrios and Schaer, Roger and Müller, Henning (2014) Crowdsourcing for Medical Image Classification. In: 27th Annual congress of the Swiss Society for Medical Informatics, ? - ?.

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Abstract

To help manage the large amount of biomedical images produced, image information retrieval tools have been developed to help access the right information at the right moment. To provide a test bed for image retrieval evaluation, the ImageCLEFmed benchmark proposes a biomedical classification task that automatically focuses on determining the image modality of figures from biomedical journal articles. In the training data for this machine learning task, some classes have many more images than others and thus a few classes are not well represented, which is a challenge for automatic image classification. To address this problem, an automatic training set expansion was first proposed. To improve the accuracy of the automatic training set expansion, a manual verification of the training set is done using the crowdsourcing platform Crowdflower. This platform allows the use of external persons to pay for the crowdsourcing or to use personal contacts free of charge. Crowdsourcing requires strict quality control or using trusted persons but it can quickly give access to a large number of judges and thus improve many machine learning tasks. Results show that the manual annotation of a large amount of biomedical images carried out in this project can help with image classification.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Notes: keywords: crowdsourcing, image classification, ImageCLEF, Medical image retrieval
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 07 Jul 2021 09:04
Last Modified: 15 Jan 2022 01:21
URI: http://repository.essex.ac.uk/id/eprint/22230

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