在皮层表面上的球形变压器
Jiale Cheng1,2, Xin Zhang1, Fenqiang Zhao2
1South China University of Technology, Guangzhou, China.
概括
我们介绍了球形变压器,这是一种用于分析大脑皮层表面数据的新型深度学习模型. 这种高效的架构有效地捕捉了神经成像应用的远程依赖和时空模式.
科学领域:
- 神经成像是一种神经成像.
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 变压器架构在计算机视觉方面取得了巨大的成功.
- 将这些模型扩展到非欧几里德空间,如大脑皮层表面,这是非常理想的.
- 皮层表面数据通常由具有球形拓的三角网格表示.
研究的目的:
- 提出一种新的球形变压器架构,用于分析大脑皮层表面数据.
- 为了利用自我注意机制来编码远程依赖和时空模式.
- 为了评估模型在神经影像预测任务上的表现.
主要方法:
- 将皮质表面映射到球体上,并将它们划分为重叠的球形斑块.
- 在本地补丁中应用自我注意力,以实现高效的计算和上下文信息保存.
- 引入时空自我注意力来处理纵向皮质表面和捕捉动态模式.
主要成果:
- 球形变压器在表面级 (认知预测) 和顶点级 (皮层厚度图开发) 预测任务上表现出了竞争力.
- 该模型有效地编码了远程依赖关系,并保留了详细的上下文信息.
- 时空自我注意力有效地提取空间背景和动态发展模式.
结论:
- 拟议的球形变压器是神经成像分析的有效通用骨干.
- 它在处理皮质表面数据,特别是纵向数据方面提供了显著的改进.
- 该模型显示了早期大脑发育绘制应用的前景.
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