metadata
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 (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
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 |
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
@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}
}