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Real-time surgical phase recognition is a fundamental task in modernoperating rooms. Previous works tackle this task relying on architecturesarranged in spatio-temporal order, however, the supportive benefits ofintermediate spatial features are not considered. In this paper, we introduce,for the first time in surgical workflow analysis, Transformer to reconsider theignored complementary effects of spatial and temporal features for accuratesurgical phase recognition. Our hybrid embedding aggregation Transformer fusescleverly designed spatial and temporal embeddings by allowing for activequeries based on spatial information from temporal embedding sequences. Moreimportantly, our framework processes the hybrid embeddings in parallel toachieve a high inference speed. Our method is thoroughly validated on two largesurgical video datasets, i.e., Cholec80 and M2CAI16 Challenge datasets, andoutperforms the state-of-the-art approaches at a processing speed of 91 fps.

Trans-SVNet: Accurate Phase Recognition from Surgical Videos via Hybrid Embedding Aggregation Transformer