Project-MONAI / Project-MONAI/MONAI
Aleatoric and Epistemic Uncertainty Engine
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Contribution wanted
Feature request
- Dominant language
- Python
- Stars
- 8.7k
- Forks
- 1.6k
- Avg merge
- 5d 1h
- Merged PRs (30d)
- 20
Description
Is your feature request related to a problem? Please describe.
Create an inferer for aleatoric and epistemic uncertainty.
Describe the solution you'd like
The inferer can be based on the TestTimeAugmentation that is already implemented and MC-Dropout uncertainty.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reading the existing TestTimeAugmentation implementation and the MC-Dropout uncertainty approach. Clarify the API and expected behavior for aleatoric and epistemic uncertainty before choosing how the inferer should combine them. Done means the inferer supports both uncertainty types with tests covering the expected outputs.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100