  Surgical instrument segmentation is extremely important for computer-assisted
surgery. Different from common object segmentation, it is more challenging due
to the large illumination and scale variation caused by the special surgical
scenes. In this paper, we propose a novel bilinear attention network with
adaptive receptive field to solve these two challenges. For the illumination
variation, the bilinear attention module can capture second-order statistics to
encode global contexts and semantic dependencies between local pixels. With
them, semantic features in challenging areas can be inferred from their
neighbors and the distinction of various semantics can be boosted. For the
scale variation, our adaptive receptive field module aggregates multi-scale
features and automatically fuses them with different weights. Specifically, it
encodes the semantic relationship between channels to emphasize feature maps
with appropriate scales, changing the receptive field of subsequent
convolutions. The proposed network achieves the best performance 97.47% mean
IOU on Cata7 and comes first place on EndoVis 2017 by 10.10% IOU overtaking
second-ranking method.
