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Crowdsourcing Experiment - RSNA 2017

Background:

Many Cancers routinely identified by imaging haven’t yet benefited from recent advances in computer science. Approaches such as machine learning and deep learning can generate quantitative tumor 3D volumes, complex features and therapy-tracking temporal dynamics. However, cross-disciplinary researchers striving to develop new approaches often lack disease understanding or sufficient contacts within the medical community. Their research can greatly benefit from labeling and annotating basic information in the images such as tumor locations, which are obvious to radiologists.

Crowdsourcing the creation of publicly-accessible reference data sets could address this challenge. In 2011 the National Cancer Institute funded development of The Cancer Imaging Archive (TCIA), a free and open-access database of medical images. However, most of these collections lack the labeling and annotations needed by image processing researchers for progress in deep learning and radiomics. With this booth we sought to harness the vast knowledge of RSNA meeting attendees to generate these tumor markups.

Data resulting from this experiment is available in the following formats:

References:  

2017 Website:  http://rsnacrowdquant2.eastus2.cloudapp.azure.com/

Statistics: http://rsnacrowdquant2.eastus2.cloudapp.azure.com/dashboard.html

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