Project-MONAI / Project-MONAI/MONAI
Evidencial Fully Convolutional Network
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- Dominant language
- Python
- Stars
- 8.7k
- Forks
- 1.6k
- Avg merge
- 5d 1h
- Merged PRs (30d)
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Description
Hi!
Is your feature request related to a problem? Please describe.
Dealing with uncertainty of ground truth seems crucial for medical imaging.
Describe the solution you'd like
An implementation of the Evidencial Fully Convolutional Network ( https://arxiv.org/pdf/2103.13544.pdf) would be great !
Which seems to require two layers :
- One Dempster Shafer Layer
- One utility layer
It has been successfully applied in medical imaging here (using MONAI as a backend): https://arxiv.org/abs/2104.13293
Best,
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 Evidential Fully Convolutional Network paper and the linked medical-imaging application, then compare their Dempster-Shafer and utility layers with MONAI's PyTorch-based architecture. No repository files, entry points, or tests are named; done would require an agreed implementation scope and validation of both layers for medical imaging.
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
- 25/100