  Surgical phase recognition is a fundamental task in computer-assisted surgery
systems. Most existing works are under the supervision of expensive and
time-consuming full annotations, which require the surgeons to repeat watching
videos to find the precise start and end time for a surgical phase. In this
paper, we introduce timestamp supervision for surgical phase recognition to
train the models with timestamp annotations, where the surgeons are asked to
identify only a single timestamp within the temporal boundary of a phase. This
annotation can significantly reduce the manual annotation cost compared to the
full annotations. To make full use of such timestamp supervisions, we propose a
novel method called uncertainty-aware temporal diffusion (UATD) to generate
trustworthy pseudo labels for training. Our proposed UATD is motivated by the
property of surgical videos, i.e., the phases are long events consisting of
consecutive frames. To be specific, UATD diffuses the single labelled timestamp
to its corresponding high confident ( i.e., low uncertainty) neighbour frames
in an iterative way. Our study uncovers unique insights of surgical phase
recognition with timestamp supervisions: 1) timestamp annotation can reduce 74%
annotation time compared with the full annotation, and surgeons tend to
annotate those timestamps near the middle of phases; 2) extensive experiments
demonstrate that our method can achieve competitive results compared with full
supervision methods, while reducing manual annotation cost; 3) less is more in
surgical phase recognition, i.e., less but discriminative pseudo labels
outperform full but containing ambiguous frames; 4) the proposed UATD can be
used as a plug and play method to clean ambiguous labels near boundaries
between phases, and improve the performance of the current surgical phase
recognition methods.
