  Automatic surgical phase recognition plays a vital role in robot-assisted
surgeries. Existing methods ignored a pivotal problem that surgical phases
should be classified by learning segment-level semantics instead of solely
relying on frame-wise information. This paper presents a segment-attentive
hierarchical consistency network (SAHC) for surgical phase recognition from
videos. The key idea is to extract hierarchical high-level semantic-consistent
segments and use them to refine the erroneous predictions caused by ambiguous
frames. To achieve it, we design a temporal hierarchical network to generate
hierarchical high-level segments. Then, we introduce a hierarchical
segment-frame attention module to capture relations between the low-level
frames and high-level segments. By regularizing the predictions of frames and
their corresponding segments via a consistency loss, the network can generate
semantic-consistent segments and then rectify the misclassified predictions
caused by ambiguous low-level frames. We validate SAHC on two public surgical
video datasets, i.e., the M2CAI16 challenge dataset and the Cholec80 dataset.
Experimental results show that our method outperforms previous
state-of-the-arts and ablation studies prove the effectiveness of our proposed
modules. Our code has been released at: https://github.com/xmed-lab/SAHC.
