一个深度学习框架从动态功能连接中定位发性区域,使用组合的图形卷积和变压器网络.
Naresh Nandakumar1, David Hsu2, Raheel Ahmed3
1Department of Electrical and Computer Engineering, Johns Hopkins University, USA.
Proceedings. IEEE International Symposium on Biomedical Imaging
|October 25, 2024
概括
这项研究引入了一个自动化框架,使用静止状态fMRI (rs-fMRI) 的动态功能连接来确定患者的发性区域 (EZ). 这种新的方法结合了图形卷积和变压器网络,以提高本地化准确度.
科学领域:
- 神经成像是一种神经成像.
- 的研究研究.
- 机器学习在医学中的应用
背景情况:
- 精确定位发性区域 (EZ) 对于治疗耐药性至关重要.
- 休息状态功能性MRI (rs-fMRI) 揭示了动态的大脑连接模式.
- 现有的EZ本地化方法存在局限性.
研究的目的:
- 开发和验证使用rs-fMRI的动态功能连接来实现EZ本地化的第一个自动化框架.
- 利用先进的深度学习技术,提高的诊断准确性.
主要方法:
- 一个集成图形卷积网络 (GCN) 的自动化框架,用于特征提取和具有注意力机制的变压器网络.
- 利用来自rs-fMRI数据的动态功能连接.
- 经过人体结合体项目的增强数据的训练,并根据临床数据集进行评估.
主要成果:
- 与废弃和基线模型相比,开发的框架在定位EZ方面表现优异.
- GCN和变压器网络的组合显著提高了定位准确性.
- 数据增强策略对于模型训练和概括是有益的.
结论:
- 拟议的自动化框架有效地利用rs-fMRI的动态功能连接来实现EZ本地化.
- 这种方法为的诊断和手术规划提供了一个有希望的非侵入性工具.
- 这些发现突显了深度学习在推进神经系统疾病研究方面的潜力.
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