Knowledge Commons of Institute of Automation,CAS
DFR-Net: A Novel Multi-Task Learning Network for Real-Time Multi-Instrument Segmentation | |
Zhou, Yan-Jie1,2![]() ![]() ![]() ![]() | |
2021-10 | |
会议名称 | ACM International Conference on Multimedia (ACM MM) |
会议日期 | 2021.10.20-24 |
会议地点 | 中国成都 |
出版者 | ACM |
摘要 | In computer-assisted vascular surgery, real-time multi-instrument segmentation serves as a pre-requisite step. However, a large amount of effort has been dedicated to single-instrument rather than multi-instrument in computer-assisted intervention research to this day. To fill the overlooked gap, this study introduces a Light-Weight Deep Feature Refinement Network (DFR-Net) based on multi-task learning for real-time multi-instrument segmentation. In this network, the proposed feature refinement module (FRM) can capture long-term dependencies while retaining precise positional information, which helps the model locate the foreground objects of interest. The designed channel calibration module (CCM) can re-calibrate fusion weights of multi-level features, which helps the model balance the importance of semantic information and appearance information. Besides, the connectivity loss function is developed to address fractures in the wire-like structure segmentation results. Extensive experiments on two different types of datasets consistently demonstrate that DFR-Net can achieve state-of-the-art segmentation performance while meeting the real-time requirements. |
关键词 | neural networks instrument segmentation multi-task learning |
收录类别 | EI |
资助项目 | National Natural Science Foundation of China[61533016] ; Strategic Priority Research Program of Chinese Academy of Science[XDB32040000] ; National Natural Science Foundation of China[U1613210] ; National Natural Science Foundation of China[61533016] ; Strategic Priority Research Program of Chinese Academy of Science[XDB32040000] ; National Natural Science Foundation of China[U1613210] |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/48544 |
专题 | 复杂系统认知与决策实验室_先进机器人 |
通讯作者 | Hou, Zeng-Guang |
作者单位 | 1.State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, CAS 2.School of Artificial Intelligence, University of Chinese Academy of Sciences 3.Center for Excellence in Brain Science and Intelligence Technology 4.CASIA-MUST Joint Laboratory of Intelligence Science and Technology, MUST |
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 | Zhou, Yan-Jie,Liu, Shi-Qi,Xie, Xiao-Liang,et al. DFR-Net: A Novel Multi-Task Learning Network for Real-Time Multi-Instrument Segmentation[C]:ACM,2021. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
3-DFR-Net A Novel Mu(2606KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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