Project-MONAI / Project-MONAI/MONAI
Improve HoVerNet postprocessing performance
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- Dominant language
- Python
- Stars
- 8.7k
- Forks
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- Avg merge
- 5d 1h
- Merged PRs (30d)
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Description
HoVerNet post-processing is an essential part of the hoverne pipeline but it is taking too much time (way more than inference itself). @JHancox has some experiences to make it run much faster but outside of MONAI. Although I am working with cuCIM team to get the necessary scikit-image functions accelerated on GPU to be able to run the postprocessing on GPU, it would be great if we can take advantage of some accelerations on CPU while GPU is busy running the inference.
CC @Nic-Ma @KumoLiu
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
The issue names the HoVerNet post-processing pipeline but no files or tests. Start by locating that path and measuring its CPU time relative to inference, then review the scikit-image functions and the proposed cuCIM acceleration work. Done means a measured improvement to CPU-side post-processing without changing its results.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- computer-vision, performance
- Issue type
- Refactor
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 28/100