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微波乳房传感通过深度学习通过概率地图进行瘤空间定位.

Marijn Borghouts1, Michele Ambrosanio2, Stefano Franceschini3

  • 1Department of Biomedical Engineering, Technical University of Eindhoven, 5600 MB Eindhoven, The Netherlands.

Bioengineering (Basel, Switzerland)
|October 28, 2023
PubMed
概括

这项研究引入了微波成像 (MWI) 的深度学习模型,直接从散射矩阵创建瘤概率图. 这种人工智能方法显著改善了MWI扫描中的乳腺癌检测和定位精度.

关键词:
生物医学工程 生物医学工程乳腺癌 乳腺癌 乳腺癌早期检测 早期检测微波成像技术 微波成像技术神经网络的神经网络的神经网络瘤定位的局部化

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 生物医学工程 生物医学工程

背景情况:

  • 微波成像 (MWI) 显示了乳腺癌查的潜力,因为它的成本效益,速度,安全性和舒适性.
  • 目前MWI的局限性包括低分辨率和检测能力,阻碍了实际的瘤检测和定位.

研究的目的:

  • 使用卷积神经网络 (CNN) 直接从分散矩阵开发准确的瘤概率图.
  • 创建一个独立于特定图像形成技术的概率图,增强MWI的适用性.
  • 用MWI改善瘤检测和局部化乳腺癌查.

主要方法:

  • 开发了一个卷积神经网络 (CNN) 模型,将散射矩阵转换为瘤概率图.
  • 深度学习模型被训练在一个大,现实的2D乳房切片的数值数据集上.
  • 模型性能使用视觉检查和定量测量来评估预测准确度.

主要成果:

  • 美国有线电视新闻网 (CNN) 模型在分类健康与疾病的个人资料方面取得了高准确度 (0.9995).
  • 该模型证明了单个瘤核心的准确定位,大多数在0.9厘米范围内.
  • 该研究证实了该模型能够提供详细的预测质量评估的能力.

结论:

  • 从散射矩阵到瘤概率图的基于神经网络的转换显示了推动MWI瘤检测的前景.
  • 这种方法有可能克服目前MWI分辨率和检测能力的局限性.
  • 这些发现表明,基于MWI的乳腺癌查算法取得了重大进展.