Welcome to grand-challenge.org’s documentation!¶
In the era of Deep Learning, developing robust machine learning solutions to problems in biomedical imaging requires access to large amounts of annotated training data, objective comparisons of state of the art machine learning solutions, and clinical validation using real world data. Grand Challenge can assist Researchers, Data Scientists, and Clinicians in collaborating to develop these solutions by providing:
- Archives
Manage medical imaging data.
- Reader Studies
Train experts and have them annotate medical imaging data.
- Challenges
Gather and objectively assess machine learning solutions.
- Algorithms
Deploy machine learning solutions for clinical validation.
Here, you find the documentation for the Django application that powers Grand Challenge. You are able to use the instance there, add additional features by making a PR to our GitHub repository, or spin up your own instance.
Contents:
- Architecture
- Development
- Evaluation
- Components
- Workstations
FeedbackFeedbackGroupObjectPermissionFeedbackUserObjectPermissionSessionSessionGroupObjectPermissionSessionUserObjectPermissionWorkstationWorkstationGroupObjectPermissionWorkstationImageWorkstationImageGroupObjectPermissionWorkstationImageUserObjectPermissionWorkstationUserObjectPermissiondelete_workstation_groups_hook()
- Reader Studies
- Creating a Reader Study
- Cases
- Defining the Hanging List
- Questions
- Adding Ground Truth
AnswerAnswerGroupObjectPermissionAnswerTypeAnswerUserObjectPermissionCategoricalOptionDisplaySetDisplaySetGroupObjectPermissionDisplaySetUserObjectPermissionImagePortInteractiveAlgorithmChoicesOptionalHangingProtocolReaderStudyQuestionQuestionGroupObjectPermissionQuestionUserObjectPermissionQuestionWidgetKindChoicesReaderStudyReaderStudyGroupObjectPermissionReaderStudyPermissionRequestReaderStudyUserObjectPermissionWorkstationSessionReaderStudydelete_reader_study_groups_hook()
- Design decisions