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可解释的AI驱动模型用于胃肠道癌症分类.

Faisal Binzagr1

  • 1Department of Computer Science, King Abdulaziz University, Rabigh, Saudi Arabia.

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|April 30, 2024
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概括
此摘要是机器生成的。

可解释的人工智能 (XAI) 通过解决"黑子"问题来增强癌细胞检测. 在KvasirV2数据集上使用SHAP与混合CNN模型实现了93.17%的准确性,提高了临床信任.

关键词:
这就是 SHAP SHAP 的意思.组合学习组合学习可以解释的人工智能AI这是胃肠道癌症.转移学习转移学习

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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
  • 计算病理学计算病理学

背景情况:

  • 由于不透明的决策过程,人工智能辅助的癌细胞检测面临临临床采用挑战.
  • 可解释的人工智能 (XAI) 通过在AI预测中提供透明度,对建立信任至关重要.
  • 人工智能的"黑盒子"性质阻碍了其融入临床工作流程.

研究的目的:

  • 用可解释的人工智能 (XAI) 提高AI模型用于癌细胞检测的可解释性.
  • 为了研究SHapley添加式扩展 (SHAP) 方法的有效性,以解释AI在癌症病理学中的预测.
  • 开发和评估一种混合深度学习模型,用于准确和可解释的癌细胞检测.

主要方法:

  • 开发了一个混合深度学习模型,结合了来自三个卷积神经网络 (CNN) 的预测:InceptionV3,InceptionResNetV2和VGG16.
  • 该模型是使用KvasirV2数据集进行训练的,该数据集包含癌症的病理图像.
  • 为了解释训练混合模型的预测,应用了夏普利增量扩展 (SHAP) 技术.

主要成果:

  • 混合CNN模型在检测癌症相关的病理症状方面获得了93.17%的高精度和97%的F1得分.
  • SHAP分析为模型的预测提供了视觉解释,突出了影响分类的图像区域.
  • 该研究证明了XAI的成功应用,以消除癌细胞检测中的AI决策过程的神秘性.

结论:

  • 集成XAI,特别是SHAP,显著提高了人工智能驱动的癌症检测系统的透明度和可靠性.
  • 混合CNN模型与XAI相结合,为临床环境中准确和可解释的癌细胞检测提供了强大的方法.
  • 这项工作通过提供可解释的预测,促进临床医生的信心,解决了在医疗保健中采用人工智能的关键障碍.