fidoptLittle W-Net
Little W-Net, a small W-shaped network for retinal vessels, with its authors' DRIVE model, run by their predict_one_image.py: test-time flips, the threshold 0.42 they chose on DRIVE.
SegmentationCPUversion lwnet ce72dda, experiments/wnet_drive (sha256 91f0cada)FUNDUSMIT
Run it
With its KonfAI app, at the benchmark's settings (the fidopt app repositories are private for now: a Hugging Face token with access opens them):
pip install konfai-apps konfai-apps infer fideus-labs/fidopt-segmentation:LWNet -i image.nii.gz -o out
The app on Hugging Face: its model, its settings and how it is scored.
Where it works
Its place on each kind of problem it has results on, among the methods ranked there. A method that leads one problem can trail another: read the one closest to yours.
| Problem | Place | Ranked | Datasets |
|---|---|---|---|
| Retinal vessels on fundus photographs, 2D segmentation | 1 | of 1 | 1 of 1 |
Results
| Dataset | # | Read by | Score | Failed | Time s |
|---|---|---|---|---|---|
| FIVES, retinal vessels drawn by hand | 1 | Dice | 0.716 | 0 | 3 |
| STARE, retinal vessels drawn by hand | 1 | Dice | 0.786 | 0 | 3 |
- Reference
- Galdran et al., Scientific Reports 2022