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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
PubMed
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.

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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German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
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