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Description
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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.
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A full explanation of the project can be seen in the booth posters:
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Download
Data resulting from this experiment is available in the following formats:
- Source DICOM scans annotated by participants
- CrowdsCureCancer2017.tcia (requires TCIA Download Manager software)
- DICOM metadata and X/Y/Z measurement coordinatesDICOM Structured Reports
- Coming soon
- Web application