Project-MONAI / Project-MONAI/MONAI
Develop a dataset for global stain extraction
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- Dominant language
- Python
- Stars
- 8.7k
- Forks
- 1.6k
- Avg merge
- 5d 1h
- Merged PRs (30d)
- 20
Description
Stain normalization requires a stain matrix that are particular to an image or a set of image acquired under the same condition and settings. In MONAI pathology, we have this functionality to extract the stain matrix from a patch of a whole slide image (WSI) with deconvolution; however, to make this process robust, we need to have pipeline to perform it on all the patches of one WSI (or several WSIs), which requires a specific dataset that handles loading and feeding all patches into this transform.
This dataset shares a lot of similarities with a general patch-based inference dataset so it would be better to design and implement such inference dataset with having this application in mind.
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
Review the existing general patch-based inference dataset and the pathology stain deconvolution functionality mentioned in the issue. Define how the new dataset should load patches from one or several WSIs and feed them into the transform pipeline; done means it supports robust global stain-matrix extraction across those patches.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- data, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100