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在基于深度学习的乳房图分析中对基于 Saliency 的可解释人工智能 (XAI) 方法进行定量评估.

Esma Cerekci1, Deniz Alis2, Nurper Denizoglu3

  • 1Sisli Hamidiye Etfal Training and Research Hospital, Department of Radiology, Istanbul, Turkey.

European journal of radiology
|February 16, 2024
PubMed
概括

在乳腺癌检测中基于突出性的可解释人工智能 (XAI) 方法的定量评估显示出局限性. 虽然这些方法提供了一些洞察力,但它们往往无法准确地确定深度学习模型所做的诊断决策.

关键词:
乳腺癌 乳腺癌 乳腺癌深度学习 (Deep Learning) 是一种深度学习.进行乳房显微镜.在XAI,XAI就是XAI.

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科学领域:

  • 医学成像分析 医学成像分析
  • 医疗保健中的人工智能
  • 放射学 放射学是一门学科.

背景情况:

  • 可解释的人工智能 (XAI) 对于理解医学诊断中的深度学习 (DL) 模型至关重要.
  • 度方法是医学成像中的常见XAI工具,但它们的性能缺乏定量验证.
  • 这项研究解决了对乳腺癌检测这些方法的严格评估的需要.

研究的目的:

  • 量化评估使用乳房影像检测乳腺癌的流行突出XAI技术的性能.
  • 为 XAI 在临床诊断支持中的可靠性提供基于证据的见解.

主要方法:

  • 一个数据集由1496张乳房影像 (癌症阳性和阴性) 进行了策划,由三个放射科医生进行了基准真相注释.
  • 在MLO和CC视图上使用预训练的深度学习模型来检测乳腺癌.
  • 度方法 (Grad-CAM,Grad-CAM++,Eigen-CAM) 使用指向游戏指标与地面真相界限框进行了评估.

主要成果:

  • 深度学习模型在测试组中实现了69%的回忆,88%的精度,80%的准确性和0.77的F1-Score.
  • 在所有癌症患者中,Grad-CAM,Grad-CAM++和Eigen-CAM的指针游戏分数分别为0.41,0.30和0.35.
  • 当仅考虑真阳性病例时,得分略有改善至0.41,0.31和0.36.

结论:

  • 基于 Saliency 的 XAI 方法为乳房镜中的深度学习模型提供了部分可解释性.
  • 这些方法在准确地界定乳腺癌检测DL模型的决策过程方面存在局限性.
  • 需要进一步的研究来开发更可靠的XAI技术用于临床应用.