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Automatic surgical phase recognition plays a vital role in robot-assistedsurgeries. Existing methods ignored a pivotal problem that surgical phasesshould be classified by learning segment-level semantics instead of solelyrelying on frame-wise information. This paper presents a segment-attentivehierarchical consistency network (SAHC) for surgical phase recognition fromvideos. The key idea is to extract hierarchical high-level semantic-consistentsegments and use them to refine the erroneous predictions caused by ambiguousframes. To achieve it, we design a temporal hierarchical network to generatehierarchical high-level segments. Then, we introduce a hierarchicalsegment-frame attention module to capture relations between the low-levelframes and high-level segments. By regularizing the predictions of frames andtheir corresponding segments via a consistency loss, the network can generatesemantic-consistent segments and then rectify the misclassified predictionscaused by ambiguous low-level frames. We validate SAHC on two public surgicalvideo datasets, i.e., the M2CAI16 challenge dataset and the Cholec80 dataset.Experimental results show that our method outperforms previousstate-of-the-arts and ablation studies prove the effectiveness of our proposedmodules. Our code has been released at: https://github.com/xmed-lab/SAHC.

Exploring Segment-level Semantics for Online Phase Recognition from Surgical Videos