使用主动学习模型进行自动化阿尔茨海默病检测,并使用强化学习和范围丧失功能
Zhisen He1, Vijay Govindarajan2, Jing Yang3
1Department of Electrical, Computer, and Systems Engineering, Case Western Reserve University, Cleveland, OH, USA.
Npj mental health research
|October 8, 2025
概括
这项研究引入了早期阿尔茨海默病 (AD) 检测的积极学习框架,通过更少的标记脑图像显著提高了模型准确性. 这种新的方法使用深度强化学习 (DRL) 来实现更高效和更适应的数据选择.
科学领域:
- 神经学 神经学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 阿尔茨海默病 (AD) 检测对于管理至关重要,但传统方法需要广泛的标记图像数据.
- 目前用于AD检测的积极学习方法通常由于静态数据选择策略而缺乏适应性.
研究的目的:
- 开发一个创新的积极学习框架,用于早期发现阿尔茨海默病,需要更少的标记样本.
- 通过使用先进的机器学习技术,提高AD检测的模型性能和适应性.
主要方法:
- 一个新的主动学习框架,将深度强化学习 (DRL) 与范围丧失函数 (SLF) 结合起来,用于动态数据选择.
- 集成差异演变 (DE) 算法,以减轻DRL组件中的超参数灵敏度.
- 对阿尔茨海默病检测OASIS和ADNI数据集的评估.
主要成果:
- 与传统方法相比,拟议的框架在早期发现阿尔茨海默病方面表现优越.
- 在OASIS数据集上达到92.044%的高F值,在ADNI数据集上达到93.685%.
- 动态数据选择策略有效地平衡了数据利用和探索,提高了模型效率.
结论:
- 开发的积极学习框架为早期发现阿尔茨海默病提供了更有效和更适应的方法.
- 这种方法显著减少了对大型,预先标记的数据集的需求,解决了医学图像分析的关键挑战.
- DRL,SLF和DE的组合为神经退行性疾病诊断提供了有前途的进展.
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