Dual Invariance Self-training for Reliable Semi-supervised Surgical Phase Recognition
Journal:
arXiv
Published Date:
Jan 29, 2025
Abstract
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.
