Bai, L;
Wang, G;
Wang, J;
Yang, X;
Gao, H;
Liang, X;
Wang, A;
... Ren, H; + view all
(2024)
OSSAR: Towards Open-Set Surgical Activity Recognition in Robot-assisted Surgery.
In:
Proceedings - IEEE International Conference on Robotics and Automation.
(pp. pp. 14622-14629).
IEEE: Yokohama, Japan.
Preview |
Text
OSSAR.pdf - Accepted Version Download (770kB) | Preview |
Abstract
In the realm of automated robotic surgery and computer-assisted interventions, understanding robotic surgical activities stands paramount. Existing algorithms dedicated to surgical activity recognition predominantly cater to pre-defined closed-set paradigms, ignoring the challenges of real-world open-set scenarios. Such algorithms often falter in the presence of test samples originating from classes unseen during training phases. To tackle this problem, we introduce an innovative Open-Set Surgical Activity Recognition (OSSAR) framework. Our solution leverages the hyperspherical reciprocal point strategy to enhance the distinction between known and unknown classes in the feature space. Additionally, we address the issue of over-confidence in the closed set by refining model calibration, avoiding misclassification of unknown classes as known ones. To support our assertions, we establish an open-set surgical activity benchmark utilizing the public JIGSAWS dataset. Besides, we also collect a novel dataset on endoscopic submucosal dissection for surgical activity tasks. Extensive comparisons and ablation experiments on these datasets demonstrate the significant outperformance of our method over existing state-of-the-art approaches. Our proposed solution can effectively address the challenges of real-world surgical scenarios. Our code is publicly accessible at github.com/longbai1006/OSSAR.
| Type: | Proceedings paper |
|---|---|
| Title: | OSSAR: Towards Open-Set Surgical Activity Recognition in Robot-assisted Surgery |
| Event: | 2024 IEEE International Conference on Robotics and Automation (ICRA) |
| Dates: | 13 May 2024 - 17 May 2024 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ICRA57147.2024.10610246 |
| Publisher version: | https://doi.org/10.1109/ICRA57147.2024.10610246 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Training, Medical robotics, Codes, Automation, Refining, Surgery, Activity recognition |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10197048 |
Archive Staff Only
 |
View Item |
