Project-MONAI / Project-MONAI/MONAILabel
Selective Inference Calling
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 891
- Forks
- 269
- Avg merge
- 15h 41m
- Merged PRs (30d)
- 1
Description
Could we change the inference app call to allow it to skip an image under specific conditions? For instance, we do not want to trigger the inference label when we add a new secondary capture to Xnat.
It would also be useful if we could run batch inference on all images of one type (i.e. non-contrast CT), again preserving the ability to skip derived / secondary images.
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
No files or tests are named. Start by locating the inference app call and the XNAT image-ingestion path, then determine how secondary or derived images are identified and how batch selection is represented. Done means selective skipping and batch inference by image type are defined, implemented, and covered by tests.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100