  We propose Masked-Attention Transformers for Surgical Instrument Segmentation
(MATIS), a two-stage, fully transformer-based method that leverages modern
pixel-wise attention mechanisms for instrument segmentation. MATIS exploits the
instance-level nature of the task by employing a masked attention module that
generates and classifies a set of fine instrument region proposals. Our method
incorporates long-term video-level information through video transformers to
improve temporal consistency and enhance mask classification. We validate our
approach in the two standard public benchmarks, Endovis 2017 and Endovis 2018.
Our experiments demonstrate that MATIS' per-frame baseline outperforms previous
state-of-the-art methods and that including our temporal consistency module
boosts our model's performance further.
