MAMVCL:用于自闭症谱系障碍分类的多地图导向多视图对比学习
Zuohao Yin1, Feng Xu1, Yue Ma2
1College of Information Science and Technology & Artificial Intelligence, Nanjing Forestry University, Nanjing 210037, China.
Brain sciences
|October 29, 2025
概括
这项研究引入了一种新的Multi-Atlas引导多视图对比学习 (MAMVCL) 框架,用于自闭症谱系障碍 (ASD) 的分类. 该模型实现了85.71%的准确性,提高了早期干预的诊断精度.
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:
- 自闭症谱系障碍 (ASD) 是一种神经发育状况,具有显著的早期儿童可塑性.
- 早期干预,包括行为治疗,语言和社交技能培训,可以减轻ASD症状.
- 准确的ASD分类对于及时有效的干预至关重要.
研究的目的:
- 为ASD分类引入一个新的多地图导向多视图对比学习 (MAMVCL) 框架.
- 利用来自多个大脑图谱的功能连接 (FC) 矩阵来提高诊断准确性.
- 整合成像和表型数据,以实现全面的ASD诊断方法.
主要方法:
- 该MAMVCL框架集成成像和表型数据使用人口图形结构.
- 图形卷积提取全球特征,而目标意识的注意力聚合器捕获局部大脑区域的依赖性.
- 一个图形对比学习策略使全球和本地特征表示保持一致.
主要成果:
- 在ABIDE-I数据集上,MAMVCL模型实现了85.71%的准确性,用于ASD分类.
- 与现有方法相比,拟议的框架显示出更高的性能.
- 实验结果证实了多图表和多视图学习方法的有效性.
结论:
- 在ASD分类中,MAMVCL模型显示出卓越的性能.
- 多图谱和多视角学习方法具有显著的潜力,可以提高ASD的诊断精度.
- 这些发现支持为患有自闭症的人制定改进的早期干预策略.
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