通过重量扰动在神经网络中实现不确定性估计,以改进阿尔茨海默氏症疾病分类
Matteo Ferrante1, Tommaso Boccato1, Nicola Toschi1,2
1Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy.
Frontiers in neuroinformatics
|February 21, 2024
概括
本研究将标准神经网络转换为贝叶斯神经网络,以估计预测不确定性. 将此与拒绝方法相结合,可以提高阿尔茨海默病检测的分类准确性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 医疗成像医学成像
背景情况:
- 深度学习模型往往缺乏预测不确定性估计.
- 对人工智能预测的信任在各个领域至关重要.
- 不确定性量化对于可靠的AI部署至关重要.
研究的目的:
- 开发一种将标准神经网络转换为贝叶斯神经网络的方法.
- 通过采样类似网络来估计预测的变化.
- 将不确定性估计与基于拒绝的分类方法相结合.
主要方法:
- 将标准神经网络转换为贝叶斯神经网络.
- 使用可调整的基于拒绝的策略,使用不确定性值.
- 将模型应用于用于阿尔茨海默氏症疾病分类的大脑图像.
主要成果:
- 实现了分类准确度从0.86增加到0.95.
- 保留了75%的测试数据,同时提高了准确性.
- 证明模型能够识别不确定的预测,以便专家审查.
结论:
- 估计预测不确定性可以提高深度学习的可靠性.
- 调节网络信任可以提高用户的信任和集成.
- 这一框架促进了AI在临床决策中的使用.
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