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首页> 外文期刊>Sensing and imaging >CSM‑Net: A Multi‑Task Colorectal Cancer Analysis Framework
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CSM‑Net: A Multi‑Task Colorectal Cancer Analysis Framework

机译:CSM-Net:多任务结肠直肠癌分析框架

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摘要

Colorectal cancer is the second leading cause of cancer mortality before 2020, in this paper, we propose a simple yet efficient anchor-free instance segmentation framework, Colorectal Spatial Mask Network, aiming at jointly detecting and segmenting the tumor region in colorectal cancer MRI image series. we add a novel spatial attention-guided mask branch to anchor-free one stage object detector into the framework with the same way in Mask R-CNN. In addition, we also employ an improved network VoVNetV2 as the new backbone network. After comparison with the state-of-the-arts methods, the results of experiments reveal that our framework can get well balance between speed and accuracy.
机译:结肠直肠癌是2020年之前的癌症死亡率的第二个主要原因,本文提出了一种简单且有效的锚定实例分割框架,结直肠空间掩模网络,旨在共同检测和分割结直肠癌MRI图像系列中的肿瘤区。我们在掩模R-CNN中,添加了一种新的空间注意引导掩模分支以将一个阶段对象探测器锚固到框架中,以相同的方式。此外,我们还使用改进的网络Vovnetv2作为新的骨干网。在与最先进的方法比较之后,实验结果表明,我们的框架可以在速度和准确性之间变得良好平衡。

著录项

  • 来源
    《Sensing and imaging》 |2020年第1期|42.1-42.14|共14页
  • 作者单位

    School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China;

    School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China;

    Institute of Computing Technology Chinese Academy of Sciences Beijing China;

    School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China;

    Zhou-Kou-Dian Middle School Beijing China;

    Heilongjiang Civil Affairs Information Service Center Harbin China;

    College of Arts and Sciences Northeast Agricultural University Harbin China;

    Diagnostic Radiology and Nuclear Medicine University of Maryland Baltimore MD USA;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Colorectal cancer; MRI image analysis; CSM-Network; Deep Learning;

    机译:结直肠癌;MRI图像分析;CSM网络;深度学习;

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