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多尺度和空间信息提取用于瘤细分:一个上下文可变形的注意力和边缘增强的U-Net

Shamija Sherryl R M R1, Jaya T2

  • 1Department of Electronics & Communication Engineering, Ponjesly College of Engineering, Nagercoil, Tamil Nadu, India. shamijasherryl48@gmail.com.

Journal of imaging informatics in medicine
|February 12, 2024
PubMed
概括

一个新的轻量级深度学习模型CDA2E-Net提高了瘤细分的准确性. 它提高了边缘定义,并适应各种瘤形状,克服了复杂现有方法的局限性.

关键词:
边缘增强损失功能的功能.瘤细分 瘤细分 瘤细分医学图像 医学图像 医学图像多尺度特征学习的学习特征是多尺度的.这就是U-Net.

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