| ---
|
|
|
| license: other
|
| license_name: research-and-education-only
|
| license_link: https://datasets.simula.no/kvasir/
|
| library_name: timm
|
| pipeline_tag: image-classification
|
| tags:
|
| - medical-imaging
|
| - endoscopy
|
| - kvasir
|
| - polyp
|
| - research-only
|
| - not-a-medical-device
|
| ---
|
|
|
| # ACPolyp-v1 (ACG-ConvNeXt-T, Kvasir v2)
|
|
|
| **Research prototype. Not a medical device. No clinical validation.**
|
|
|
| The weights were trained on Kvasir v2 (Simula), which is licensed for research and education only.
|
| Commercial use of this model is not permitted without Simula's prior written permission.
|
| The code is MIT-licensed; these weights are **not**, because they inherit the dataset restriction.
|
| The Kvasir images are not included.
|
|
|
| ## What it is
|
|
|
| ConvNeXt-T (27.9 M parameters, ImageNet-22k to 1k pretrained, Apache-2.0) with the ACGNet head: backbone
|
| features pooled inside and outside an unsupervised active-contour mask, fused with 98 contour features,
|
| 8 Kvasir classes. Temperature scaling and a split-conformal quantile are stored in `calibration.json`.
|
|
|
| ## Honest results
|
|
|
| * Bundled model: run `ab_acg/seed0`, chosen by validation macro-F1. Test macro-F1 0.9315, accuracy 0.9316,
|
| polyp-vs-rest AUROC 0.9886, ECE 0.0312 (after temperature scaling).
|
| * That is one seed. The 3-seed mean of the contour-guided model is 0.9277 against 0.9233 for plain ConvNeXt-T:
|
| the difference is **not statistically supported** (Holm p = 0.30). Contour guidance did not help.
|
| * The conformal quantile in the bundle was calibrated on the test split (validation was reused for model
|
| selection and under-covers), so the test numbers above are not an unbiased estimate of conformal coverage.
|
|
|
| ## Limits
|
|
|
| Single-centre data, no patient identifiers (scene-grouped split is the best available leakage guard), no
|
| external validation, esophagitis vs z-line is the main confusion, and a fraction of Kvasir labels are suspect
|
| (see the audit in the repository).
|
|
|
| ## Use
|
|
|
| Load with the `acpolyp` package from the code repository: `python -m acpolyp.cli predict --help`.
|
| The folder is verified by SHA-256 in `manifest.json`.
|
|
|
| ## Citation
|
|
|
| Cite the code repository (see `CITATION.cff` there) and the datasets:
|
| Pogorelov et al., MMSys 2017, doi:10.1145/3083187.3083212; Jha et al., MMM 2020, doi:10.1007/978-3-030-37734-2_37.
|
| |
