通过MRI进行多标签诊断的神经认知潜空间规范化
Jocasta Manasseh-Lewis1, Felipe Godoy1, Wei Peng1
1Stanford University, Stanford, CA 94305.
概括
在脑MRI研究中,深度学习的解释性通过根据临床变量安排潜在空间来提高. 这种方法提高了分类的准确性,并与神经科学发现保持一致.
科学领域:
- 神经成像是一种神经成像.
- 人工智能的人工智能
- 认知神经科学 认知神经科学
背景情况:
- 深度学习模型对于使用脑MRI进行神经科学发现至关重要.
- 这些模型的可解释性对于可靠的科学结论至关重要.
- 目前的方法缺乏足够的对齐与临床上有意义的变量在潜空间.
研究的目的:
- 为了提高大脑MRI研究中的深度学习模型的解释性.
- 为了调整多标签分类器的隐藏空间,使用对式解.
- 为了使隐性空间表示与神经心理测试得分保持一致.
主要方法:
- 应用双向解来规范多标签分类器的潜空间.
- 使用来自对照组,轻度认知障碍 (MCI) 和艾滋病毒相关认知障碍 (HAND) 病例的大脑MRI数据.
- 与神经心理学z-score (NPZ) 相比,解开了隐藏空间.
主要成果:
- 与没有脱的模型相比,拟议的脱方法在统计学上取得了显著更高的平衡精度.
- 沿着解方向的潜空间表示的差异与NPZ分数的差异有显著的相关性.
- 确定了对分类至关重要的脑区域,这些区域与现有的神经科学文献保持一致.
结论:
- 配对解有效地提高了大脑MRI分析中的深度学习模型的解释性.
- 该方法提供了更具临床意义的潜空间表示,与认知状态相关联.
- 这种方法为神经科学发现和理解认知障碍提供了一个有前途的工具.
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