摘要
由一名新闻记者兼机器人与机器学习的新闻编辑每日新闻-一项关于机器人的新研究现在开始了。据N ewsRx编辑在中国北京发表的新闻报道称，“医学成像机器人通常使用X射线、磁共振成像(MRI)和计算机断层扫描(CT)等技术来生成人体内部的图像。这些生成的图像复杂，含有大量的噪声和干扰。”这需要高分辨率和实时快速图像分析算法来提取重要的信息，包括肿瘤区域、肿瘤位置、器官和组织以及血液血管信息。本文引用北京交通大学的一篇研究文章,提出了一种新的轻量级神经网络对脑MRI图像进行分割,实现了高精度和快速的分割,并针对实时性的要求,提出了一种基于通道注意机制的轻量级神经网络模块。为了丰富特征映射信息,本文设计了一种空间注意机制,将编码器和解码器的输出特征映射相对应地连接起来,实现了对特征映射信息的丰富。通过对比实验和烧蚀研究，提高了模型的有效性，使模型具有更高的性能。
Abstract
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 .”
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