OperA: Attention-Regularized Transformers for Surgical Phase Recognition
Czempiel T, Paschali M, Ostler D, Kim ST, Busam B, Navab N (2021)
Publication Type: Conference contribution
Publication year: 2021
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 12904 LNCS
Pages Range: 604-614
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Virtual, Online
ISBN: 9783030872014
DOI: 10.1007/978-3-030-87202-1_58
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.
Involved external institutions
Germany (DE)
Korea, Republic of (KR)How to cite
APA:
Czempiel, T., Paschali, M., Ostler, D., Kim, S.T., Busam, B., & Navab, N. (2021). OperA: Attention-Regularized Transformers for Surgical Phase Recognition. In Marleen de Bruijne, Marleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 604-614). Virtual, Online: Springer Science and Business Media Deutschland GmbH.
MLA:
Czempiel, Tobias, et al. "OperA: Attention-Regularized Transformers for Surgical Phase Recognition." Proceedings of the 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, Virtual, Online Ed. Marleen de Bruijne, Marleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert, Springer Science and Business Media Deutschland GmbH, 2021. 604-614.
BibTeX: Download