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RAUNet: Residual Attention U-Net for Semantic Segmentation of Cataract Surgical Instruments [article]

Zhen-Liang Ni, Gui-Bin Bian, Xiao-Hu Zhou, Zeng-Guang Hou, Xiao-Liang Xie, Chen Wang, Yan-Jie Zhou, Rui-Qi Li, Zhen Li
<span title="2019-10-02">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
To the best of our knowledge, this is the first cataract surgical instrument dataset for semantic segmentation.  ...  In this paper, an attention-guided network is proposed to segment the cataract surgical instrument.  ...  As far as we know, this is the first cataract surgery instrument dataset that can be used for semantic segmentation. 2 Residual Attention U-Net Overview High-resolution images provide more detailed location  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1909.10360v3">arXiv:1909.10360v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/bzvw3yjs4jedtdrcqzr5x3rnpi">fatcat:bzvw3yjs4jedtdrcqzr5x3rnpi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200829185524/https://arxiv.org/pdf/1909.10360v3.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/62/ac/62ac0c27328fdfa46f7ba13177085f29e2c47c79.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1909.10360v3" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

DeepPyram: Enabling Pyramid View and Deformable Pyramid Reception for Semantic Segmentation in Cataract Surgery Videos [article]

Negin Ghamsarian, Mario Taschwer, klaus Schoeffmann
<span title="2021-09-11">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Semantic segmentation in cataract surgery has a wide range of applications contributing to surgical outcome enhancement and clinical risk reduction.  ...  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.  ...  This paper presents a U-Net-based CNN for semantic segmentation that can adaptively capture the semantic information in cataract surgery videos.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2109.05352v1">arXiv:2109.05352v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/n5u6crsgszblfgxnuxobzzgy6i">fatcat:n5u6crsgszblfgxnuxobzzgy6i</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210918010235/https://arxiv.org/pdf/2109.05352v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/7b/fe/7bfe2ceadd61a40dfb9909f75c473fe15ab35842.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2109.05352v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

BARNet: Bilinear Attention Network with Adaptive Receptive Fields for Surgical Instrument Segmentation [article]

Zhen-Liang Ni, Gui-Bin Bian, Guan-An Wang, Xiao-Hu Zhou, Zeng-Guang Hou, Xiao-Liang Xie, Zhen Li, Yu-Han Wang
<span title="2020-05-22">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Surgical instrument segmentation is extremely important for computer-assisted surgery.  ...  For the illumination variation, the bilinear attention module can capture second-order statistics to encode global contexts and semantic dependencies between local pixels.  ...  This issue makes the instrument segmentation more challenging. Recently, a series of methods have been proposed for the semantic segmentation of surgical instruments.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2001.07093v4">arXiv:2001.07093v4</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zm7jf633l5brbhubr5j62oexsq">fatcat:zm7jf633l5brbhubr5j62oexsq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200527193551/https://arxiv.org/pdf/2001.07093v4.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2001.07093v4" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

BARNet: Bilinear Attention Network with Adaptive Receptive Fields for Surgical Instrument Segmentation

Zhen-Liang Ni, Gui-Bin Bian, Guan-An Wang, Xiao-Hu Zhou, Zeng-Guang Hou, Xiao-Liang Xie, Zhen Li, Yu-Han Wang
<span title="">2020</span> <i title="International Joint Conferences on Artificial Intelligence Organization"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/vfwwmrihanevtjbbkti2kc3nke" style="color: black;">Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence</a> </i> &nbsp;
Surgical instrument segmentation is crucial for computer-assisted surgery.  ...  With them, semantic features in challenging areas can be inferred from their neighbors, and the distinction of various semantics can be boosted.  ...  Acknowledgments Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.24963/ijcai.2020/116">doi:10.24963/ijcai.2020/116</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/ijcai/NiBWZHXLW20.html">dblp:conf/ijcai/NiBWZHXLW20</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xct4te4e7ngsbgu2pcazlcnx4q">fatcat:xct4te4e7ngsbgu2pcazlcnx4q</a> </span>
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