Summary
Add support for nnUNet ensemble within MONet-Bundle to enable model ensembling using nnUNet predictions and workflows.
Motivation
Supporting nnUNet ensemble can improve segmentation performance by combining results from multiple models, a method widely adopted in medical imaging challenges. This will increase the flexibility and effectiveness of MONet-Bundle when working with nnUNet-trained models.
Expected Functionality
- Allow users to run nnUNet ensembles as part of the MONet-Bundle workflow.
- Provide configuration options for different ensemble schemes (e.g., majority voting, averaging).
- Documentation and examples on how to enable and use nnUNet ensembling.
Acceptance Criteria
- A user can configure and trigger an nnUNet ensemble through MONet-Bundle.
- Proper documentation is provided.
- Unit and/or integration tests cover the new ensemble workflow.
Additional Context
- Reference nnUNet's existing ensemble modules for implementation inspiration.
- Ensure compatibility with MONet-Bundle’s current design.
Summary
Add support for nnUNet ensemble within MONet-Bundle to enable model ensembling using nnUNet predictions and workflows.
Motivation
Supporting nnUNet ensemble can improve segmentation performance by combining results from multiple models, a method widely adopted in medical imaging challenges. This will increase the flexibility and effectiveness of MONet-Bundle when working with nnUNet-trained models.
Expected Functionality
Acceptance Criteria
Additional Context