OperA: Attention-Regularized Transformers for Surgical Phase Recognition

Author:

Czempiel Tobias,Paschali Magdalini,Ostler Daniel,Kim Seong Tae,Busam Benjamin,Navab Nassir

Publisher

Springer International Publishing

Reference28 articles.

1. Maier-Hein, L., et al.: Surgical data science: a consensus perspective. arXiv preprint arXiv:1806.03184 (2018)

2. Garrow, C.R., et al.: Machine learning for surgical phase recognition: a systematic review. Ann. Surg. 273, 684–693 (2020)

3. Padoy, N.: Machine and deep learning for workflow recognition during surgery. Minim. Invasive Ther. Allied Technol. 28, 82–90 (2019)

4. Huaulmé, A., Jannin, P., Reche, F., Faucheron, J.L., Moreau-Gaudry, A., Voros, S.: Offline identification of surgical deviations in laparoscopic rectopexy. Artif. Intell. Med. 104(May), 2020 (2019)

5. Twinanda, A.P., Shehata, S., Mutter, D., Marescaux, J., De Mathelin, M., Padoy, N.: EndoNet: a deep architecture for recognition tasks on laparoscopic videos. IEEE Trans. Med. Imaging 36(1), 86–97 (2017)

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