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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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一个新的可解释的AI框架用于医疗图像分类,集成统计,视觉和基于规则的方法.

Naeem Ullah1, Florentina Guzmán-Aroca2, Francisco Martínez-Álvarez3

  • 1Department of Electrical Engineering and Information Technology, University of Naples Federico II, via Claudio 21, Naples, 80125, Italy.

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|June 12, 2025
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概括

这项研究引入了一种新的可解释的人工智能 (AI) 方法,用于医学图像分析. 它提高了深度学习模型的透明度,使用集成的统计,视觉和基于规则的解释.

关键词:
可解释的人工智能功能工程的特点工程.医学图像分类 医学图像分类基于规则的解释性

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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
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 深度学习模型在医疗数据分析方面表现出色,但由于其"黑子"性质,缺乏透明度.
  • 现有的可解释AI (XAI) 方法仅通过可视化或基于规则的系统提供有限的解释性.
  • 在高风险的医疗应用中,解释AI决策至关重要.

研究的目的:

  • 为医疗图像分析开发一种新的XAI方法,该方法集成了统计,视觉和基于规则的解释.
  • 提高医疗图像分类中的深度学习模型的透明度和可解释性.
  • 为临床医生提供对人工智能驱动的诊断过程的更深入的见解.

主要方法:

  • 一个定制的Mobilenetv2模型从医疗图像中提取了深度特征.
  • 一个两步的特征选择 (基于零的过和相互重要性的选择) 提炼了提取的特征.
  • 决策树和RuleFit模型生成人类可读的规则,并补充了一个新的统计特征地图叠加可视化 (平均值,斜率,).

主要成果:

  • 拟议的XAI方法在五个不同的医学成像数据集 (COVID-19,乳腺癌,脑瘤,肺癌/结肠癌,玻璃眼) 中得到了验证.
  • 综合方法提供了本地化和可量化的视觉解释,提高了模型的透明度.
  • 结果得到了医学专家的证实,表明了实际的实用性.

结论:

  • 新的XAI方法显著提高了医疗图像分类中的深度学习模型的可解释性.
  • 统计,视觉和基于规则的解释的整合提供了对AI决策的更全面的理解.
  • 这种方法有望增加AI在临床环境中的信任和采用.