Competition projectColonoscopy · Segmentation
Colonoscopy Polyp Segmentation
A multi-dataset study of polyp masks in colonoscopy frames, built around reproducible preprocessing and comparative segmentation experiments.
Project record
This page reports the maturity and evidence documented in Ethosoft's working archive. It does not imply peer review, regulatory clearance, or clinical validation.
Methods and evidence
Colonoscopy Polyp Segmentation
A multi-dataset study of polyp masks in colonoscopy frames, built around reproducible preprocessing and comparative segmentation experiments.
- Models explored
- Attention U-Net, U-Net++, DeepLabV3+, PVT-Cascade, and SSFormer-S with a progressive refinement network.
- Ablations
- Learning-rate comparisons, bilateral filtering, elastic deformation, image generators, and cosine-annealing warm restarts.
- Reported results
- The report records U-Net++ validation mean IoU of 0.8907 and PolypDB mean IoU of 0.8655, alongside uneven results across external datasets.
Generalization note: the documented cross-dataset variance is retained because aggregate validation performance alone can hide distribution shift.