论文标题
BARNET：双线性注意网络，具有自适应接收场，用于手术仪器分割
BARNet: Bilinear Attention Network with Adaptive Receptive Fields for Surgical Instrument Segmentation
论文作者
论文摘要
手术仪器分割对于计算机辅助手术极为重要。与普通对象分割不同，由于特殊手术场景引起的较大的照明和比例变化，它更具挑战性。在本文中，我们提出了一个具有自适应接受场的新型双线性注意网络，以解决这两个挑战。对于照明变化，双线性注意模块可以捕获二阶统计信息以编码本地像素之间的全局上下文和语义依赖性。有了他们，可以从邻居中推断出具有挑战性的地区的语义特征，并且可以提高各种语义的区别。对于刻度变化，我们的自适应接收场模块聚集了多尺度特征，并自动将其与不同的权重融合。具体而言，它编码通道之间的语义关系，以使用适当的尺度强调特征图，从而改变了随后的卷积的接受场。拟议的网络达到了最佳性能97.47％的意思是CATA7上的IOU，并且在Endovis 2017上排名第一，IOU超过了第二级方法。
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.
