Project-MONAI / Project-MONAI/MONAI
Semantic-Aware Image Augmentation Strategy
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- 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.
In the MONAI Core service, one might need, sometimes to have more images in the training dataset in order to minimize the generalization error and bias. I'm working on an augmentation policy using Pytorch.
Describe the solution you'd like
The solution is to define a set of augmentation techniques and try to find the subset of methods (including nested combinations) that augment the data without violating the semantic aspect.
Describe alternatives you've considered
None
Additional context
Trying to apply this to blood cells from a bone marrow smear.
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, tests, or entry points are identified in the issue. Start by surveying the MONAI augmentation-related components and existing PyTorch integration, then define how candidate techniques and nested combinations will be evaluated. Done means a documented or implemented strategy that increases training data without violating the semantic content of the bone-marrow smear images.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- computer-vision, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100