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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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基于注意力的多尺度嵌套网络用于生物医学图像细分.

Dapeng Cheng1,2, Jia Deng1, Jinjie Xiao1

  • 1School of Computer Science and Technology, Shandong Business and Technology University, No. 191 Binhai Middle Road, Yantai, 264000, Shandong Province, China.

Heliyon
|December 13, 2024
PubMed
概括
此摘要是机器生成的。

基于注意力的多尺度嵌套网络 (AMNNet) 通过解决样本变异性来改善生物医学图像细分. 这种新的架构增强了特征重点和多尺度上下文,在多个数据集上优于现有的方法.

关键词:
这就是为什么CBAM是CBAM.卷积神经网络是一种卷积神经网络.医疗图像细分 医疗图像细分剩余的U-CBAM模块可以使用.

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科学领域:

  • 医疗成像医学成像
  • 计算机视觉 计算机视觉
  • 人工智能的人工智能

背景情况:

  • 卷积神经网络 (CNN) 已经在生物医学图像细分方面取得了先进.
  • 医疗图像细分面临的挑战是由于样本内部的变化和样本间的特异性.
  • 现有的模型往往忽视了医学图像细分的独特挑战.

研究的目的:

  • 提出一种新的架构,即基于注意力的多尺度嵌套网络 (AMNNet),用于高效的生物医学图像细分.
  • 解决当前模型在医疗数据集的样本内变异性处理方面的局限性.
  • 为了提高医疗图像细分的准确性和效率.

主要方法:

  • 开发了AMNNet,结合了剩余U-CBAM (RSUC) 模块,卷积阶段,MLP阶段和卷积块注意模块 (CBAM).
  • 引入了一个轻量级的CBAM,以专注于相关地区,并抑制无关的特征.
  • 设计的RSUC模块通过结合不同的受体场来捕获多尺度的上下文信息.

主要成果:

  • 与现有方法相比,AMNNet在多个基准数据集 (ISIC2018,CVC-ClinicDB,CVC-ColonDB,BUSI,Glass) 中表现优越.
  • 实现了高的子相似度系数 (DSC):91.35% (ISIC2018),90.01% (CVC-ClinicDB),90.80% (CVC-ColonDB),81.61% (BUSI) 和94.31% (GlaS). 获得了高的子相似度系数 (DSC):91.35% (ISIC2018),90.01% (CVC-ClinicDB),90.80% (CVC-ColonDB),81.61% (BUSI) 和94.31% (GlaS). 获得了高的子相似度系数 (DSC) 获得了高的子相似度系数 (DSC).
  • 拟议的架构有效地处理了医疗图像固有的特异性和可变性.

结论:

  • 对于具有挑战性的生物医学图像细分任务,AMNNet提供了一个有效的解决方案.
  • 注意力机制和多级处理的整合提高了细分的准确性.
  • AMNNet为自动化医疗图像分析提供了一个有希望的进步.