Saved in:
| Main Authors: | , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.24897 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917362314248192 |
|---|---|
| author | Meng, Yan Cook, Jack Han, X. Y. Duman, Kaan Otto, Shauna Pangal, Dhiraj Chainey, Jonathan Lau, Ruth Masson-Forsythe, Margaux Donoho, Daniel A. Levy, Danielle Zada, Gabriel Froelich, Sébastien Fernandez-Miranda, Juan Chang, Mike |
| author_facet | Meng, Yan Cook, Jack Han, X. Y. Duman, Kaan Otto, Shauna Pangal, Dhiraj Chainey, Jonathan Lau, Ruth Masson-Forsythe, Margaux Donoho, Daniel A. Levy, Danielle Zada, Gabriel Froelich, Sébastien Fernandez-Miranda, Juan Chang, Mike |
| contents | Accurate surgical phase recognition is essential for analyzing procedural workflows, supporting intraoperative decision-making, and enabling data-driven improvements in surgical education and performance evaluation. In this work, we present a comprehensive framework for phase recognition in pituitary tumor surgery (PTS) videos, combining self-supervised representation learning, robust temporal modeling, and scalable data annotation strategies. Our method achieves 90\% accuracy on a held-out test set, outperforming current state-of-the-art approaches and demonstrating strong generalization across variable surgical cases.
A central contribution of this work is the integration of a collaborative online platform designed for surgeons to upload surgical videos, receive automated phase analysis, and contribute to a growing dataset. This platform not only facilitates large-scale data collection but also fosters knowledge sharing and continuous model improvement. To address the challenge of limited labeled data, we pretrain a ResNet-50 model using the self-supervised framework on 251 unlabeled PTS videos, enabling the extraction of high-quality feature representations. Fine-tuning is performed on a labeled dataset of 81 procedures using a modified training regime that incorporates focal loss, gradual layer unfreezing, and dynamic sampling to address class imbalance and procedural variability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_24897 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SurgPhase: Time efficient pituitary tumor surgery phase recognition via an interactive web platform Meng, Yan Cook, Jack Han, X. Y. Duman, Kaan Otto, Shauna Pangal, Dhiraj Chainey, Jonathan Lau, Ruth Masson-Forsythe, Margaux Donoho, Daniel A. Levy, Danielle Zada, Gabriel Froelich, Sébastien Fernandez-Miranda, Juan Chang, Mike Computer Vision and Pattern Recognition Accurate surgical phase recognition is essential for analyzing procedural workflows, supporting intraoperative decision-making, and enabling data-driven improvements in surgical education and performance evaluation. In this work, we present a comprehensive framework for phase recognition in pituitary tumor surgery (PTS) videos, combining self-supervised representation learning, robust temporal modeling, and scalable data annotation strategies. Our method achieves 90\% accuracy on a held-out test set, outperforming current state-of-the-art approaches and demonstrating strong generalization across variable surgical cases. A central contribution of this work is the integration of a collaborative online platform designed for surgeons to upload surgical videos, receive automated phase analysis, and contribute to a growing dataset. This platform not only facilitates large-scale data collection but also fosters knowledge sharing and continuous model improvement. To address the challenge of limited labeled data, we pretrain a ResNet-50 model using the self-supervised framework on 251 unlabeled PTS videos, enabling the extraction of high-quality feature representations. Fine-tuning is performed on a labeled dataset of 81 procedures using a modified training regime that incorporates focal loss, gradual layer unfreezing, and dynamic sampling to address class imbalance and procedural variability. |
| title | SurgPhase: Time efficient pituitary tumor surgery phase recognition via an interactive web platform |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.24897 |