基于Grad-CAM的可解释的人工智能与医学文本处理相关
Hongjian Zhang1, Katsuhiko Ogasawara1
1Graduate School of Health Science, Hokkaido University, N12-W5, Kitaku, Sapporo 060-0812, Japan.
Bioengineering (Basel, Switzerland)
|September 28, 2023
概括
可解释的人工智能 (XAI) 通过使用梯度加权类激活映射 (Grad-CAM) 来增强医学深度学习,以实现直观的决策. ResNet模型在医学文本分类中实现了高精度,提高了模型的透明度.
科学领域:
- 人工智能的人工智能
- 医疗信息学 医疗信息学
- 计算机视觉 计算机视觉
背景情况:
- 深度学习的不透明性阻碍了医疗应用,需要可解释AI (XAI).
- XAI确保人类对医疗保健中人工智能模型决策的理解.
- 想象模型的注意力对于信任和验证至关重要.
研究的目的:
- 开发和评估用于医学文本分类的XAI系统.
- 提高医疗保健中的深度学习模型的透明度.
- 为了直观地呈现AI驱动的医学文本分析的基础.
主要方法:
- 从计算机视觉 (ResNet) 转移学习到医疗文本任务.
- 使用梯度加权类激活映射 (Grad-CAM) 进行可视化.
- 将Word2Vec,BERT,ResNet,1D CNN和Bi-LSTM进行比较,以进行文本分类.
主要成果:
- 在正式医疗文本上进行预训练的ResNet获得了最高的性能 (90.9%的回忆,91.1%的精度,90.2%的F1得分).
- 在模型预测中,Grad-CAM可视化有效地突出了重要的单词.
- 开发的系统证明了高精度的分类与可解释的结果.
结论:
- ResNet与Grad-CAM相结合,为医学文本分类提供了一个强大的XAI解决方案.
- 这种方法提高了AI在医学中的解释性和可靠性.
- 这种方法为医疗应用的AI决策过程提供了直观的见解.
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