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RAUNet: Residual Attention U-Net for Semantic Segmentation of Cataract Surgical Instruments
[article]
2019
arXiv
pre-print
Semantic segmentation of surgical instruments plays a crucial role in robot-assisted surgery. However, accurate segmentation of cataract surgical instruments is still a challenge due to specular reflection and class imbalance issues. In this paper, an attention-guided network is proposed to segment the cataract surgical instrument. A new attention module is designed to learn discriminative features and address the specular reflection issue. It captures global context and encodes semantic
arXiv:1909.10360v3
fatcat:bzvw3yjs4jedtdrcqzr5x3rnpi