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arXiv · 2302.10834

Weakly Supervised Temporal Convolutional Networks for Fine-grained Surgical Activity Recognition

Sanat Ramesh·Diego Dall'Alba·Cristians Gonzalez·Tong Yu·Pietro Mascagni·Didier Mutter·Jacques Marescaux·Paolo Fiorini·Nicolas Padoy
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Abstract

Automatic recognition of fine-grained surgical activities, called steps, is a challenging but crucial task for intelligent intra-operative computer assistance. The development of current vision-based activity recognition methods relies heavily on a high volume of manually annotated data. This data is difficult and time-consuming to generate and requires domain-specific knowledge. In this work, we propose to use coarser and easier-to-annotate activity labels, namely phases, as weak supervision to learn step recognition with fewer step annotated videos. We introduce a step-phase dependency loss to exploit the weak supervision signal. We then employ a Single-Stage Temporal Convolutional Network (SS-TCN) with a ResNet-50 backbone, trained in an end-to-end fashion from weakly annotated videos, for temporal activity segmentation and recognition. We extensively evaluate and show the effectiveness of the proposed method on a large video dataset consisting of 40 laparoscopic gastric bypass procedures and the public benchmark CATARACTS containing 50 cataract surgeries.

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Sanat Ramesh, Diego Dall'Alba, Cristians Gonzalez, Tong Yu, Pietro Mascagni, Didier Mutter, Jacques Marescaux, Paolo Fiorini, Nicolas Padoy. 2023-04-11. Weakly Supervised Temporal Convolutional Networks for Fine-grained Surgical Activity Recognition. https://doi.org/10.1109/tmi.2023.3262847

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