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不确定性引导的变压器用于脑瘤细分.

Zan Chen1, Chenxu Peng1, Wenlong Guo1

  • 1College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China.

Medical & biological engineering & computing
|September 4, 2023
PubMed
概括

本研究引入了一种以不确定性为导向的变压器,用于使用多模式数据进行强大的脑瘤细分. 这种新的方法通过专注于不确定的地区来提高细分精度,在BraTS2021数据集上表现优于现有的方法.

关键词:
变压器变压器变压器瘤细分 瘤的细分不确定性面罩是什么?

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科学领域:

  • 医疗图像分析 医学图像分析
  • 医学中的人工智能
  • 神经成像是一种神经成像.

背景情况:

  • 多模式数据增强了脑瘤细分,但可以引入冗余信息.
  • 瘤边界和形状对准确的细分质量估计提出了挑战.

研究的目的:

  • 利用多模式数据开发一种强大的脑瘤细分方法.
  • 为了应对冗余信息和瘤特征模两可的挑战.

主要方法:

  • 利用了一个以不确定性为导向的U形变压器,具有多个头.
  • 构建的退学格式面具 (边界,先前概率,条件概率) 针对不确定性区域的集中培训.

主要成果:

  • 在BraTS2021数据集上取得了与最先进的方法相似或更高的结果.
  • 得到的平均子系数为[公式:参见文本]和豪斯多夫距离为4.91.

结论:

  • 拟议的不确定性引导变压器有效地处理多模式数据,以改善脑瘤细分.
  • 该方法在细分具有模两可的边界和不规则形状的瘤方面表现出稳健性.