CSBNC-PAL: 一致性 半监督的大脑网络分类框架与原型-对抗性学习
IEEE journal of biomedical and health informatics
|May 12, 2025
概括
这项研究引入了一种新的半监督学习框架 (CSBNC-PAL) 用于功能大脑网络的分类. 它有效地处理多站点数据差异,通过对齐各站点的功能来提高分类准确性.
科学领域:
- 神经科学是一个神经科学.
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 半监督学习 (SSL) 通过使用未标记的多站点数据来对功能大脑网络 (FBN) 进行分类.
- 现有的SSL方法面临着不同站点分布差异的挑战,限制了特征提取和分类性能.
研究的目的:
- 提出一个新的一致性半监督的FBN分类框架与原型-对抗学习 (CSBNC-PAL).
- 解决现有的SSL方法在处理多站点数据变异方面的局限性,以改善FBN分类.
主要方法:
- 一个对比一致性模块 (CCM) 以有效地利用未标记的数据和初步的特征表示学习.
- 一个原型对齐模块 (PAM) 用于站点意识的原型计算和站点间特征对齐.
- 一个使用梯度逆转的对抗性对齐模块 (AAM) 来实现站内对齐和学习站内不变特征.
主要成果:
- 拟议的CSBNC-PAL框架整合了CCM,PAM和AAM,以实现端到端的优化.
- 该方法有效地从标记和未标记的数据中学习,同时减轻多站点数据分布差异.
- 在ABIDE I,ABIDE II和ADHD-200数据集上的实验显示,与最先进的SSL方法相比,性能优越.
结论:
- CSBNC-PAL提供了一个强大的解决方案,用于在多站点设置中进行半监督的功能性脑网络分类.
- 该框架成功地解决了站点间的分布变化,从而提高了分类准确性.
- 这些发现突显了原型-对抗性学习在推进大脑网络分析方面的潜力.
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