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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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对于弱监督的局部极端映射 关于乳房镜分类和定位的学习

Minjuan Zhu1, Lei Zhang1, Lituan Wang1

  • 1College of Computer Science, Sichuan University, Section 4, Southern 1st Ring Rd., Chengdu 610065, China.

Bioengineering (Basel, Switzerland)
|April 26, 2025
PubMed
概括

这项研究引入了一种用于乳房镜分析的新方法,该方法使用图像级标签,而不是昂贵的像素级数据. 这种方法改善了乳腺损伤的检测和定位,使自动化分析更容易获得.

关键词:
乳腺癌的分类 乳腺癌的分类深度神经网络是一个神经网络.病变的局部化 病变的局部化乳房镜像图像 乳房镜像图像监管能力较弱 监管能力较弱

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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
  • 计算机辅助诊断 计算机辅助诊断

背景情况:

  • 早期和准确的乳腺损伤检测通过乳房扫描对于患者的生存至关重要.
  • 目前的深度学习方法需要昂贵的像素级注释,这阻碍了现实世界的应用.
  • 弱监督的学习提供了一个潜在的解决方案,以减少注释成本.

研究的目的:

  • 提出一种新的局部极端映射 (LEM) 机制,用于乳房影像分类和弱监督的病变定位.
  • 为了减少对像素级注释的依赖,深度学习模型用于乳房图像.
  • 提高自动化乳腺造影分析的效率和可访问性.

主要方法:

  • 用卷积神经网络将乳房影像划分为区域并生成分数图.
  • 通过在得分图中过局部极端来识别信息区域.
  • 从信息区域汇总分数以进行最终分类和病变定位.

主要成果:

  • 在CBIS-DDSM和INbreast数据集上取得了竞争性表现.
  • 在INbreast数据集上,分类准确度提高到96.3%,AUC为0.976.
  • 有效地局部化病变,子相似系数为0.37,优于基线方法,如Grad-CAM.

结论:

  • 拟议的LEM方法只使用图像级标签,可以实现精确的乳房影像分类和病变定位.
  • 这种方法显著降低了注释成本,提供了实际的临床意义.
  • 对于潜在的临床应用,LEM提高了自动化乳腺造影分析的可访问性和效率.