SurgPhase: Time efficient pituitary tumor surgery phase recognition via an interactive web platform

• URL: http://arxiv.org/abs/2603.24897v1
• Date: Thu, 26 Mar 2026 00:22:20 GMT
• Title: SurgPhase: Time efficient pituitary tumor surgery phase recognition via an interactive web platform
• Abstract summary: We present a comprehensive framework for phase recognition in pituitary tumor surgery (PTS) videos.<n>Our method achieves 90% accuracy on a held-out test set, outperforming current state-of-the-art approaches.<n>A central contribution of this work is the integration of a collaborative online platform designed for surgeons to upload surgical videos.
• Score: 4.1977714530157435
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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