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

• URL: http://arxiv.org/abs/2501.17628v1
• Date: Wed, 29 Jan 2025 13:07:56 GMT
• Title: Dual Invariance Self-training for Reliable Semi-supervised Surgical Phase Recognition
• Authors: Sahar Nasirihaghighi, Negin Ghamsarian, Raphael Sznitman, Klaus Schoeffmann,
• Abstract summary: Semi-supervised learning, particularly with pseudo-labeling, shows promise over fully supervised methods but often lacks reliable pseudo-label assessment mechanisms. We propose a novel SSL framework, Dual Invariance Self-Training (DIST), that incorporates both Temporal and Transformation Invariance. 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.
• Score: 5.7977777220041204
• License:
• 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.

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