Knowledge Commons of Institute of Automation,CAS
SurgiNet: Pyramid Attention Aggregation and Class-wise Self-Distillation for Surgical Instrument Segmentation | |
Ni, Zhen-Liang1,2![]() ![]() ![]() ![]() ![]() ![]() | |
发表期刊 | MEDICAL IMAGE ANALYSIS
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ISSN | 1361-8415 |
2022-02-01 | |
卷号 | 76页码:102310 |
摘要 | Surgical instrument segmentation plays a promising role in robot-assisted surgery. However, illumination issues often appear in surgical scenes, altering the color and texture of surgical instruments. Changes in visual features make surgical instrument segmentation difficult. To address illumination issues, the SurgiNet is proposed to learn pyramid attention features. The double attention module is designed to capture the semantic dependencies between locations and channels. Based on semantic dependencies, the semantic features in the disturbed area can be inferred for addressing illumination issues. Pyramid attention is aggregated to capture multi-scale features and make predictions more accurate. To perform model compression, class-wise self-distillation is proposed to enhance the representation learning of the network, which performs feature distillation within the class to eliminate interference from other classes. Top-down and multi-stage knowledge distillation is designed to distill class probability maps. By inter layer supervision, high-level probability maps are applied to calibrate the probability distribution of lowlevel probability maps. Since class-wise distillation enhances the self-learning of the network, the network can get excellent performance with a lightweight backbone. The proposed network achieves the state-of-the-art performance of 89.14% mIoU on CataIS with only 1.66 GFlops and 2.05 M parameters. It also takes first place on EndoVis 2017 with 66.30% mIoU. |
关键词 | Surgical Insturment Segmentation Class-wise Self-Distillation Pyramid Attention |
DOI | 10.1016/j.media.2021.102310 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[62027813] ; National Natural Science Foundation of China[62003343] ; National Natural Science Foundation of China[U1913210] ; National Natural Science Foundation of China[U1913601] ; National Natural Science Foundation of China[U1713220] ; Beijing Science and Technology Star[Z19110 0 0 01119046] ; Youth Innovation Promotion Association of the Chinese Academy of Sciences[2018165] ; Youth Innovation Promotion Association of the Chinese Academy of Sciences[2020140] |
项目资助者 | National Natural Science Foundation of China ; Beijing Science and Technology Star ; Youth Innovation Promotion Association of the Chinese Academy of Sciences |
WOS研究方向 | Computer Science ; Engineering ; Radiology, Nuclear Medicine & Medical Imaging |
WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Radiology, Nuclear Medicine & Medical Imaging |
WOS记录号 | WOS:000742838900002 |
出版者 | ELSEVIER |
七大方向——子方向分类 | 多模态智能 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/47043 |
专题 | 复杂系统认知与决策实验室_先进机器人 |
通讯作者 | Bian, Gui-Bin; Hou, Zeng-Guang |
作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 3.Macau Univ Sci & Technol, Inst Syst Engn, Joint Lab Intelligence Sci & Technol, Macau, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Ni, Zhen-Liang,Zhou, Xiao-Hu,Wang, Guan-An,et al. SurgiNet: Pyramid Attention Aggregation and Class-wise Self-Distillation for Surgical Instrument Segmentation[J]. MEDICAL IMAGE ANALYSIS,2022,76:102310. |
APA | Ni, Zhen-Liang.,Zhou, Xiao-Hu.,Wang, Guan-An.,Yue, Wen-Qian.,Li, Zhen.,...&Hou, Zeng-Guang.(2022).SurgiNet: Pyramid Attention Aggregation and Class-wise Self-Distillation for Surgical Instrument Segmentation.MEDICAL IMAGE ANALYSIS,76,102310. |
MLA | Ni, Zhen-Liang,et al."SurgiNet: Pyramid Attention Aggregation and Class-wise Self-Distillation for Surgical Instrument Segmentation".MEDICAL IMAGE ANALYSIS 76(2022):102310. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
MIA.pdf(1944KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 |
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