稀有深度神经网络用于编码和解码结构连接器
Satya P Singh1, Sukrit Gupta2, Jagath C Rajapakse3
1Division of Electronics and Communication EngineeringNetaji Subhas University of Technology Dwarka New Delhi 110078 India.
概括
这项研究引入了一种新的稀疏深度神经网络,用于分析大脑连接体,提高诊断阿尔茨海默氏症和帕金森病的准确性,同时降低计算成本. 该方法有效地识别了关键的大脑生物标志物.
科学领域:
- 神经科学是一个神经科学.
- 机器学习 机器学习
- 医疗成像医学成像
背景情况:
- 用于神经成像的深度学习面临着高维数据和有限样本的挑战.
- 对大脑状态的准确分类对于神经退行性疾病的诊断至关重要.
研究的目的:
- 开发一种稀疏的深度神经架构,以高效地编码和解码人类大脑结构连接体.
- 为了提高阿尔茨海默病 (AD) 和帕金森病 (PD) 的分类准确度,使用DTI脑部扫描.
- 通过先进的特征选择方法识别与AD和PD相关的关键生物标志物.
主要方法:
- 一个新的稀疏前深度神经架构,具有稀疏连接的元素智能乘法第一个隐藏层和一个固定的转换输出层.
- 应用DeepLIFT,层层相关性传播 (LRP) 和集成梯度 (IG) 来进行特征相关性分析.
- 递归特征消除 (RFE) 算法用于识别关键生物标志物和删除无关特征.
主要成果:
- 拟议的稀疏架构显著减少了可训练参数 (AD的45.1%,PD的47.1%) 和训练时间,与标准的前网络相比.
- 认知正常 (CN) 与疾病分类的分类精度增加了2.6% (AD) 和3.1% (PD).
- RFE方法进一步提高了准确性 (2.1%为AD,4%为PD),同时删除了90-95%的无关特征,识别了与现有文献一致的生物标志物.
结论:
- 稀疏的深度神经架构为大脑连接组分析提供了计算效率高,准确的方法.
- 基于相关性得分的方法对大脑解码和识别疾病特异性生物标志物是有效的.
- 这种方法成功地减少了模型的复杂性,提高了分类性能,并检测了生物学上相关的大脑区域和连接.
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