标题： 双不变性自训练用于可靠的半监督手术阶段识别 显示英文标题

标题： Dual Invariance Self-training for Reliable Semi-supervised Surgical Phase Recognition

摘要： 准确的手术阶段识别对于推进计算机辅助干预至关重要，然而标注数据的稀缺性阻碍了可靠深度学习模型的训练。 半监督学习（SSL），特别是带有伪标签的方法，在完全监督方法上显示出前景，但通常缺乏可靠的伪标签评估机制。 为解决这一差距，我们提出了一种新颖的SSL框架，双不变自训练（DIST），该框架结合了时间不变性和变换不变性以增强手术阶段识别。 我们的两步自训练过程动态选择可靠的伪标签，确保稳健的伪监督。 我们的方法降低了噪声伪标签的风险，引导决策边界向真实数据分布靠拢，并提高了对未见过的数据的泛化能力。 在白内障和Cholec80数据集上的评估表明，我们的方法优于最先进的SSL方法，在各种网络架构上始终超越监督和SSL基线。 显示英文摘要

摘要： Accurate surgical phase recognition is crucial for advancing computer-assisted interventions, yet the scarcity of labeled data hinders training reliable deep learning models. Semi-supervised learning (SSL), particularly with pseudo-labeling, shows promise over fully supervised methods but often lacks reliable pseudo-label assessment mechanisms. To address this gap, we propose a novel SSL framework, Dual Invariance Self-Training (DIST), that incorporates both Temporal and Transformation Invariance to enhance surgical phase recognition. Our two-step self-training process dynamically selects reliable pseudo-labels, ensuring robust pseudo-supervision. Our approach mitigates the risk of noisy pseudo-labels, steering decision boundaries toward true data distribution and improving generalization to unseen data. Evaluations on Cataract and Cholec80 datasets show our method outperforms state-of-the-art SSL approaches, consistently surpassing both supervised and SSL baselines across various network architectures.