高 stakes 领域中基于元学习的单样本技能评估
One-shot skill assessment in high-stakes domains with limited data via meta learning
AI总结：
提出 A-VBANet 元学习模型，通过单样本学习实现领域无关的手术技能评估，在模拟和真实手术中分别达到 99.5% 和 89.7% 的准确率。
摘要：
深度学习（DL）已在各种高 stakes 领域实现了稳健的能力评估。然而，DL 模型的适用性常因其大量数据需求和对特定训练领域的限制而受阻。这阻碍了它们向数据稀缺的新任务迁移。因此，领域适应成为 DL 在实际场景中应用的关键要素。在此，我们引入了 A-VBANet，一种新颖的元学习模型，能够通过单样本学习实现领域无关的技能评估。我们通过在五个腹腔镜和机器人模拟器以及真实腹腔镜胆囊切除术上评估手术技能来测试我们的方法。我们的模型成功适应，在模拟任务中单样本设置下准确率高达 99.5%，少样本设置下高达 99.9%，在腹腔镜胆囊切除术中准确率达 89.7%。这项研究首次在关键领域实现了领域无关的技能评估方法，为 DL 在数据有限的各种真实领域中的广泛应用树立了先例。
英文摘要：
Deep Learning (DL) has achieved robust competency assessment in various high-stakes fields. However, the applicability of DL models is often hampered by their substantial data requirements and confinement to specific training domains. This prevents them from transitioning to new tasks where data is scarce. Therefore, domain adaptation emerges as a critical element for the practical implementation of DL in real-world scenarios. Herein, we introduce A-VBANet, a novel meta-learning model capable of delivering domain-agnostic skill assessment via one-shot learning. Our methodology has been tested by assessing surgical skills on five laparoscopic and robotic simulators and real-life laparoscopic cholecystectomy. Our model successfully adapted with accuracies up to 99.5% in one-shot and 99.9% in few-shot settings for simulated tasks and 89.7% for laparoscopic cholecystectomy. This study marks the first instance of a domain-agnostic methodology for skill assessment in critical fields setting a precedent for the broad application of DL across diverse real-life domains with limited data.
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。
