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In this paper, we propose a novel bilinear attention network with adaptive receptive field to solve these two challenges. ... Surgical instrument segmentation is extremely important for computer-assisted surgery. ... Conclusion In this paper, the bilinear attention network with adaptive receptive field (BARNet) is proposed for surgical instrument segmentation. ...arXiv:2001.07093v4 fatcat:zm7jf633l5brbhubr5j62oexsq
In this paper, we propose a bilinear attention network with adaptive receptive fields to address these two issues. ... Surgical instrument segmentation is crucial for computer-assisted surgery. ... Based on the above analysis, the bilinear attention network with adaptive receptive fields, named BARNet, is proposed. ...doi:10.24963/ijcai.2020/116 dblp:conf/ijcai/NiBWZHXLW20 fatcat:xct4te4e7ngsbgu2pcazlcnx4q
The proposed approach is evaluated using four datasets of cataract surgery for objects with different contextual features and compared with thirteen state-of-the-art segmentation networks. ... , which enables a wide deformable receptive field that can adapt to geometric transformations in the object of interest; and (iii) Pyramid Loss that adaptively supervises multi-scale semantic feature maps ... BARNet  adopts a bilinear-attention module to extract the cross semantic dependencies between the different channels of a convolutional feature map. ...arXiv:2109.05352v1 fatcat:n5u6crsgszblfgxnuxobzzgy6i
Martin Wagner was responsible for phase annotations, which were the basis for the frame extraction. He was further a medical expert who performed the instrument segmentations' quality control. ... The concept for the frame extraction was developed by all the mentioned people together. The annotations of the instruments were generated with the help of the entire CAMI group. ... BARNet : -, . [Ni et al., ] Zhen- : Bilinear Attention Network with Adaptive Receptive Field for Surgical Instrument Segmentation. arXiv preprint arXiv: ., . ...doi:10.11588/heidok.00030928 fatcat:qxx4qc66q5hkzhg2bd474ylhwi