SF-TMN: SlowFast Temporal Modeling Network for Surgical Phase Recognition

• URL: http://arxiv.org/abs/2306.08859v1
• Date: Thu, 15 Jun 2023 05:04:29 GMT
• Title: SF-TMN: SlowFast Temporal Modeling Network for Surgical Phase Recognition
• Authors: Bokai Zhang, Mohammad Hasan Sarhan, Bharti Goel, Svetlana Petculescu, Amer Ghanem
• Abstract summary: We propose SlowFast Temporal Modeling Network (SF-TMN) for surgical phase recognition. It can achieve frame-level full video temporal modeling and segment-level full video temporal modeling. SF-TMN with ASFormer backbone outperforms the state-of-the-art Not End-to-End(TCN) method by 2.6% in accuracy and 7.4% in the Jaccard score.
• Score: 0.5669790037378094
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatic surgical phase recognition is one of the key technologies to support Video-Based Assessment (VBA) systems for surgical education. Utilizing temporal information is crucial for surgical phase recognition, hence various recent approaches extract frame-level features to conduct full video temporal modeling. For better temporal modeling, we propose SlowFast Temporal Modeling Network (SF-TMN) for surgical phase recognition that can not only achieve frame-level full video temporal modeling but also achieve segment-level full video temporal modeling. We employ a feature extraction network, pre-trained on the target dataset, to extract features from video frames as the training data for SF-TMN. The Slow Path in SF-TMN utilizes all frame features for frame temporal modeling. The Fast Path in SF-TMN utilizes segment-level features summarized from frame features for segment temporal modeling. The proposed paradigm is flexible regarding the choice of temporal modeling networks. We explore MS-TCN and ASFormer models as temporal modeling networks and experiment with multiple combination strategies for Slow and Fast Paths. We evaluate SF-TMN on Cholec80 surgical phase recognition task and demonstrate that SF-TMN can achieve state-of-the-art results on all considered metrics. SF-TMN with ASFormer backbone outperforms the state-of-the-art Not End-to-End(TCN) method by 2.6% in accuracy and 7.4% in the Jaccard score. We also evaluate SF-TMN on action segmentation datasets including 50salads, GTEA, and Breakfast, and achieve state-of-the-art results. The improvement in the results shows that combining temporal information from both frame level and segment level by refining outputs with temporal refinement stages is beneficial for the temporal modeling of surgical phases.

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