OperA: Attention-Regularized Transformers for Surgical Phase Recognition

• URL: http://arxiv.org/abs/2103.03873v1
• Date: Fri, 5 Mar 2021 18:59:14 GMT
• Title: OperA: Attention-Regularized Transformers for Surgical Phase Recognition
• Authors: Tobias Czempiel, Magdalini Paschali, Daniel Ostler, Seong Tae Kim, Benjamin Busam, Nassir Navab
• Abstract summary: We introduce OperA, a transformer-based model that accurately predicts surgical phases from long video sequences. OperA is thoroughly evaluated on two datasets of laparoscopic cholecystectomy videos, outperforming various state-of-the-art temporal refinement approaches.
• Score: 46.72897518687539
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper we introduce OperA, a transformer-based model that accurately predicts surgical phases from long video sequences. A novel attention regularization loss encourages the model to focus on high-quality frames during training. Moreover, the attention weights are utilized to identify characteristic high attention frames for each surgical phase, which could further be used for surgery summarization. OperA is thoroughly evaluated on two datasets of laparoscopic cholecystectomy videos, outperforming various state-of-the-art temporal refinement approaches.

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