TRandAugment: temporal random augmentation strategy for surgical activity recognition from videos.
Fiche publication
Date publication
mars 2023
Journal
International journal of computer assisted radiology and surgery
Auteurs
Membres identifiés du Cancéropôle Est :
Pr MARESCAUX Jacques, Pr MUTTER Didier
Tous les auteurs :
Ramesh S, Dall'Alba D, Gonzalez C, Yu T, Mascagni P, Mutter D, Marescaux J, Fiorini P, Padoy N
Lien Pubmed
Résumé
Automatic recognition of surgical activities from intraoperative surgical videos is crucial for developing intelligent support systems for computer-assisted interventions. Current state-of-the-art recognition methods are based on deep learning where data augmentation has shown the potential to improve the generalization of these methods. This has spurred work on automated and simplified augmentation strategies for image classification and object detection on datasets of still images. Extending such augmentation methods to videos is not straightforward, as the temporal dimension needs to be considered. Furthermore, surgical videos pose additional challenges as they are composed of multiple, interconnected, and long-duration activities.
Mots clés
Cataract procedures, Data augmentation, Gastric bypass procedures, Surgical activity recognition, Temporal augmentation, Temporal convolutional networks
Référence
Int J Comput Assist Radiol Surg. 2023 03 22;: