---
license: cc-by-nc-4.0
library_name: pytorch
tags:
  - surgical-video
  - laparoscopic-cholecystectomy
  - critical-view-of-safety
  - image-classification
  - semantic-segmentation
  - knowledge-distillation
---

# EdgeCVS

Models from [**EdgeCVS: Democratization of Surgical AI with a Distilled Edge-Deployable
Critical View of Safety (CVS) Model**](https://papers.miccai.org/miccai-2026/paper/1745_paper.pdf) (MICCAI 2026).

EdgeCVS assesses the three Critical View of Safety criteria in laparoscopic cholecystectomy
from single frames, with a small model distilled from large multi-task teachers that runs in
real time on a CPU.

Code: [github.com/IMSY-DKFZ/edgecvs](https://github.com/IMSY-DKFZ/edgecvs)

## Models

Each model lives in its own folder, named like the code's `exp` option:

| Model (`exp`) | Backbone | Params | Weights | SAGES-CVS 2024 test mAP |
|---|---|---|---|---|
| [`edgecvs-5m`](edgecvs-5m/) | EdgeNeXt-Small | 5.6M | fp16, 11 MB | 66.4 |

```
edgecvs-5m/
├── config.yaml                     model configuration used to build the network
└── edgecvs-5m-fold0.safetensors    weights (fold 0, fp16; loaded into an fp32 model)
```

## EdgeCVS-5M

- **Input:** RGB frame, resized to 448×448, ImageNet normalization
- **Output:** probabilities for C1 (two structures), C2 (hepatocystic triangle) and
  C3 (cystic plate); optional 7-class anatomy segmentation (background, cystic plate,
  hepatocystic triangle, cystic artery, cystic duct, gallbladder, tool)
- **Deployment:** the segmentation head is optional; the classifier alone is used for the CSV
  predictions and the speed figures below


## License

CC BY-NC 4.0: the models are trained on data released for non-commercial use.

## Citation

```bibtex
@InProceedings{YamAmi_EdgeCVS_MICCAI2026,
        author = { Yamlahi, Amine AND Hennighausen, Jakob AND Hansen, Pascal AND Leeb, David AND Maier-Hein, Lena},
        title = { { EdgeCVS: Democratization of surgical AI with a Distilled Edge-Deployable Critical View of Safety (CVS) model } },
        booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026},
        year = {2026},
        publisher = {Springer Nature Switzerland},
        volume = {LNCS 16892},
        month = {September},
        page = {pending}
}
```