将黑盒蒸成可解释的模型,以有效地转移学习
Shantanu Ghosh1, Ke Yu2, Kayhan Batmanghelich1
1Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA.
概括
这项研究介绍了一种新的可解释的医疗保健AI模型,可以有效地适应新的数据领域. 通过将黑子模型提炼成可解释的组件,它可以以最小的数据和成本实现高性能.
科学领域:
- 医疗保健中的人工智能
- 机器学习的可解释性
- 医学成像分析 医学成像分析
背景情况:
- 可通用的人工智能模型对于医疗保健至关重要,但在数据分布转移方面遇到了困难.
- 微调人工智能模型需要在新领域广泛的标记数据.
- 与黑盒模型相比,可解释的AI模型通常表现不佳.
研究的目的:
- 开发一个可解释的人工智能模型,有效地微调到未见的领域,成本最小.
- 创建一个混合的浅层可解释模型,以实现与黑盒模型相比较的性能.
- 为了利用伪标签和微调来适应医疗AI的领域.
主要方法:
- 将一个黑盒模型蒸成一个混合的浅层,人类可以理解的可解释模型.
- 使用对可解释组件的域不变假设.
- 从半监督学习中应用伪标签来对目标领域概念分类.
- 在目标领域微调可解释模型以实现高效的适应.
主要成果:
- 可解释模型的混合实现了与黑盒模型可比的性能.
- 拟议的方法允许高效的微调到未见的领域,最小的计算成本.
- 该模型在大型胸部X射线分类数据集上证明了有效性.
结论:
- 开发的可解释的人工智能方法促进了医疗保健中高效的领域适应.
- 这种方法解决了医疗应用人工智能模型中概括性的挑战.
- 该模型的可解释性有助于理解临床环境中的AI决策.
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