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关于可解释的人工智能 (XAI) 技术的调查,用于在医学成像中可视化深度学习模型

Deepshikha Bhati¹, Fnu Neha¹, Md Amiruzzaman²

¹Department of Computer Science, Kent State University, Kent, OH 44242, USA.

Journal of imaging

|October 25, 2024

概括

医学成像中的深度学习有助于诊断,但缺乏透明度. 这项调查探讨了可解释性和可视化技术,以了解这些复杂的模型,增强信任和临床使用.

科学领域:

医疗成像医学成像
人工智能的人工智能
计算机科学计算机科学

背景情况:

深度学习模型显著提升医学成像诊断和预后.
深度学习模型的复杂性造成了"黑子"问题,阻碍了对其决策的理解.
解释性和可视化对于信任医疗保健中的AI至关重要.

研究的目的:

调查和分析医学成像中深度学习的解释和可视化技术.
为这些技术的方法,应用和有效性提供见解.
提高人工智能驱动的医学图像分析的可靠性和临床相关性.

主要方法:

关于可解释性和可视化方法的综合文献综述.
现有技术的分类和分析.
基于其解释深度学习模型的能力对技术的评估.

主要成果:

识别各种不同的解释和可视化策略.
讨论各种方法的优点和局限性.
评估这些技术如何提高模型透明度和可信度.

结论:

可解释性和可视化对于在临床实践中采用深度学习至关重要.
有效的技术可以弥合人工智能能力和临床需求之间的差距.
进一步的研究可以优化这些方法,以提高医疗AI可靠性.

关键词:

深度学习是一种深度学习.可以解释的人工智能AI 机器学习是机器学习.医学成像医学成像模型的解释性可解释性

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