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arXiv 2307.01232eess.IVcs.CVcs.LG

内窥镜视频中基于噪声数据的鲁棒手术器械检测

Robust Surgical Tools Detection in Endoscopic Videos with Noisy Data

Information Technology University（信息技术大学）；University of Bristol（布里斯托尔大学）；Bristol Heart Institute（布里斯托尔心脏研究所）；University of the West of England（西英格兰大学）；Qatar University（卡塔尔大学）

Adnan Qayyum, Hassan Ali, Massimo Caputo, Hunaid Vohra, Taofeek Akinosho, Sofiat Abioye, Ilhem Berrou, Paweł Capik, Junaid Qadir, Muhammad Bilal

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AI总结：

本文针对手术器械检测中标签噪声和数据不足问题，提出结合主动学习标签校正与师生模型自训练集成的方法，在14种器械分类上取得85.88%的平均F1分数，显著优于现有方法。

摘要：

在过去几年中，手术数据科学引起了机器学习（ML）社区的广泛关注。多项研究已经证明，新兴的机器学习技术在分析手术数据（尤其是手术过程记录）方面具有显著效果，可用于数字化临床和非临床功能，例如术前规划、上下文感知决策以及手术技能评估。然而，该领域仍处于起步阶段，缺乏具有代表性且标注良好的数据集，用于在中间机器学习任务中训练鲁棒模型。此外，现有数据集存在标签不准确的问题，阻碍了可靠模型的发展。在本文中，我们提出了一种系统化方法，用于利用噪声数据开发鲁棒的手术器械检测模型。我们的方法引入了两项关键创新：（1）一种智能主动学习策略，用于最小数据集识别和由人类专家进行标签校正；（2）一种基于学生-教师模型的自训练框架的集成策略，以半监督方式实现14种手术器械的鲁棒分类。此外，我们采用加权数据加载器来处理困难类别标签并解决类别不平衡问题。所提出的方法在使用类别权重的集成模型自训练下实现了85.88%的平均F1分数，而在噪声标签且不使用类别权重的情况下实现了80.88%的平均F1分数。同时，我们提出的方法显著优于现有方法，有效证明了其有效性。

英文摘要：

Over the past few years, surgical data science has attracted substantial interest from the machine learning (ML) community. Various studies have demonstrated the efficacy of emerging ML techniques in analysing surgical data, particularly recordings of procedures, for digitizing clinical and non-clinical functions like preoperative planning, context-aware decision-making, and operating skill assessment. However, this field is still in its infancy and lacks representative, well-annotated datasets for training robust models in intermediate ML tasks. Also, existing datasets suffer from inaccurate labels, hindering the development of reliable models. In this paper, we propose a systematic methodology for developing robust models for surgical tool detection using noisy data. Our methodology introduces two key innovations: (1) an intelligent active learning strategy for minimal dataset identification and label correction by human experts; and (2) an assembling strategy for a student-teacher model-based self-training framework to achieve the robust classification of 14 surgical tools in a semi-supervised fashion. Furthermore, we employ weighted data loaders to handle difficult class labels and address class imbalance issues. The proposed methodology achieves an average F1-score of 85.88\% for the ensemble model-based self-training with class weights, and 80.88\% without class weights for noisy labels. Also, our proposed method significantly outperforms existing approaches, which effectively demonstrates its effectiveness.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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