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

Automatic surgical phase recognition plays an important 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. In this paper, we present 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 (SFA) 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 by a large margin, notably reaches 4.1% improvements on M2CAI16. Code will be released at GitHub upon acceptance.