用自动化机器学习解决手术缝合动作检测的类别不平衡问题
An Automated Machine Learning Framework for Surgical Suturing Action Detection under Class Imbalance
- 基于自动机器学习框架，处理经验与新手医生数据的类别不平衡
- 在真实训练场景中实现快速、可靠的动作实时检测，准确率超90%
- 兼顾模型可解释性，适合医疗场景下的可信赖系统开发
在腹腔镜手术培训与评估中，实时检测手术动作并提供可解释输出对自动化实时教学反馈和技能提升至关重要，有助于构建智能引导式训练系统。本文提出一种基于经验与新手外科医生数据的快速部署自动化机器学习方法，有效应对高度不平衡的类别分布问题，确保在不同技能水平下均具备鲁棒预测能力。此外，该方法部分引入模型透明性，满足医疗应用中的可靠性要求。相较于深度学习方法，传统机器学习模型不仅支持高效快速部署，且在可解释性方面具有显著优势。实验表明，该方法具备在手术培训环境中实现快速、可靠、高效实时检测的潜力。
原文摘要 · Abstract (English)
In laparoscopy surgical training and evaluation, real-time detection of surgical actions with interpretable outputs is crucial for automated and real-time instructional feedback and skill development. Such capability would enable development of machine guided training systems. This paper presents a rapid deployment approach utilizing automated machine learning methods, based on surgical action data collected from both experienced and trainee surgeons. The proposed approach effectively tackles the challenge of highly imbalanced class distributions, ensuring robust predictions across varying skill levels of surgeons. Additionally, our method partially incorporates model transparency, addressing the reliability requirements in medical applications. Compared to deep learning approaches, traditional machine learning models not only facilitate efficient rapid deployment but also offer significant advantages in interpretability. Through experiments, this study demonstrates the potential of this approach to provide quick, reliable and effective real-time detection in surgical training environments
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