One-shot skill assessment in high-stakes domains with limited data via meta learning
arxiv(2022)
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
tasks and 89.7
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.
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