SemiVT-Surge: Semi-Supervised Video Transformer for Surgical Phase Recognition
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915318174056448 |
|---|---|
| author | Li, Yiping de Jong, Ronald Nasirihaghighi, Sahar Jaspers, Tim van Jaarsveld, Romy Kuiper, Gino van Hillegersberg, Richard van der Sommen, Fons Ruurda, Jelle Breeuwer, Marcel Khalil, Yasmina Al |
| author_facet | Li, Yiping de Jong, Ronald Nasirihaghighi, Sahar Jaspers, Tim van Jaarsveld, Romy Kuiper, Gino van Hillegersberg, Richard van der Sommen, Fons Ruurda, Jelle Breeuwer, Marcel Khalil, Yasmina Al |
| contents | Accurate surgical phase recognition is crucial for computer-assisted interventions and surgical video analysis. Annotating long surgical videos is labor-intensive, driving research toward leveraging unlabeled data for strong performance with minimal annotations. Although self-supervised learning has gained popularity by enabling large-scale pretraining followed by fine-tuning on small labeled subsets, semi-supervised approaches remain largely underexplored in the surgical domain. In this work, we propose a video transformer-based model with a robust pseudo-labeling framework. Our method incorporates temporal consistency regularization for unlabeled data and contrastive learning with class prototypes, which leverages both labeled data and pseudo-labels to refine the feature space. Through extensive experiments on the private RAMIE (Robot-Assisted Minimally Invasive Esophagectomy) dataset and the public Cholec80 dataset, we demonstrate the effectiveness of our approach. By incorporating unlabeled data, we achieve state-of-the-art performance on RAMIE with a 4.9% accuracy increase and obtain comparable results to full supervision while using only 1/4 of the labeled data on Cholec80. Our findings establish a strong benchmark for semi-supervised surgical phase recognition, paving the way for future research in this domain. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_01471 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SemiVT-Surge: Semi-Supervised Video Transformer for Surgical Phase Recognition Li, Yiping de Jong, Ronald Nasirihaghighi, Sahar Jaspers, Tim van Jaarsveld, Romy Kuiper, Gino van Hillegersberg, Richard van der Sommen, Fons Ruurda, Jelle Breeuwer, Marcel Khalil, Yasmina Al Computer Vision and Pattern Recognition Accurate surgical phase recognition is crucial for computer-assisted interventions and surgical video analysis. Annotating long surgical videos is labor-intensive, driving research toward leveraging unlabeled data for strong performance with minimal annotations. Although self-supervised learning has gained popularity by enabling large-scale pretraining followed by fine-tuning on small labeled subsets, semi-supervised approaches remain largely underexplored in the surgical domain. In this work, we propose a video transformer-based model with a robust pseudo-labeling framework. Our method incorporates temporal consistency regularization for unlabeled data and contrastive learning with class prototypes, which leverages both labeled data and pseudo-labels to refine the feature space. Through extensive experiments on the private RAMIE (Robot-Assisted Minimally Invasive Esophagectomy) dataset and the public Cholec80 dataset, we demonstrate the effectiveness of our approach. By incorporating unlabeled data, we achieve state-of-the-art performance on RAMIE with a 4.9% accuracy increase and obtain comparable results to full supervision while using only 1/4 of the labeled data on Cholec80. Our findings establish a strong benchmark for semi-supervised surgical phase recognition, paving the way for future research in this domain. |
| title | SemiVT-Surge: Semi-Supervised Video Transformer for Surgical Phase Recognition |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.01471 |