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OperA: Attention-Regularized Transformers for Surgical Phase Recognition

2021-03-05 18:59:14
Tobias Czempiel, Magdalini Paschali, Daniel Ostler, Seong Tae Kim, Benjamin Busam, Nassir Navab
arXiv_AI
           
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.

Abstract (translated)

URL

https://arxiv.org/abs/2103.03873

PDF

https://arxiv.org/pdf/2103.03873.pdf

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