OperA: Attention-Regularized Transformers for Surgical Phase Recognition - Czempiel, Tobias; Paschali, Magdalini; Ostler, Daniel; Kim, Seong Tae; Busam, Benjamin; Navab, Nassir (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2021)
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.
CRIS-Team Scopus Importer:2023-03-08
Keywords: Cholecystectomy; Self-attention; Surgical phase recognition; Surgical workflow analysis; Transformers