首页|Researchers at Beijing Jiaotong University Have Reported New Data on Robotics (A Real-time Mri Tumour Segmentation Method Based On Lightweight Network for Imagi ng Robotic Systems)
Researchers at Beijing Jiaotong University Have Reported New Data on Robotics (A Real-time Mri Tumour Segmentation Method Based On Lightweight Network for Imagi ng Robotic Systems)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on Robotics is now availab le. According to news reporting out of Beijing, People’s Republic of China, by N ewsRx editors, research stated, “Medical imaging robots typically use technologi es, such as X-ray, magnetic resonance imaging (MRI), and computed tomography (CT ), to generate images of the human body interior. These generated images are com plex and contain a large amount of noise and interference, which requires high-p recision and real -time fast image analysis algorithms to extract significant in formation, including tumour area, tumour location, organ and tissue, and blood v essel information.” Our news journalists obtained a quote from the research from Beijing Jiaotong Un iversity, “This paper proposes a novel lightweight neural network to perform tum our segmentation in brain MRI images, which could realize the high-accuracy and fast execution. To meet the real -time requirements, a lightweight module based on channel attention mechanism is presented, which constitutes an encoder-deco d er architecture for the segmentation task. To enrich the feature map information , this paper designs a spatial attention mechanism to concatenate the output fea ture maps of the encoder and decoder correspondingly, which could realize the be tter fusion of high-level and low-level semantic features extracted by the netwo rk. The comparison experiments and ablation studies are conducted to improve the effectiveness of the proposed model, which could represent a higher performance .”
BeijingPeople’s Republic of ChinaAsi aEmerging TechnologiesMachine LearningRoboticsRobotsBeijing Jiaotong U niversity
NETL
NSTL
