核磁共振扫描仪制造商对使用深度学习模型进行分类任务的影响
Rafsanjany Kushol1, Pedram Parnianpour2, Alan H Wilman3
1Department of Computing Science, University of Alberta, Edmonton, Canada. kushol@ualberta.ca.
Scientific reports
|October 5, 2023
概括
深度学习模型因扫描器变化而难以处理多中心MRI数据,这个问题称为域移位. 在这项研究中,像ComBat这样的协调技术并没有改善疾病分类性能.
科学领域:
- 神经成像是一种神经成像.
- 机器学习 机器学习
- 医学图像分析 医学图像分析
背景情况:
- 深度学习模型越来越多地用于复杂的神经科学问题的医学成像.
- 多中心研究利用更大的数据集,但由于磁共振成像 (MRI) 扫描仪特征的变化而面临挑战.
- 这些变化被称为域移动,导致机器学习模型的性能不一致.
研究的目的:
- 在不同扫描仪制造商 (GE,飞利浦,西门子) 的多中心MRI数据上分析深度学习网络的性能.
- 调查基于ComBat的协调在减轻扫描仪供应商对疾病分类任务的影响方面的有效性.
主要方法:
- 通过GE,飞利浦和西门子扫描仪的多中心3D结构MRI数据评估了多个深度学习网络.
- 在多中心数据集中应用基于ComBat的协调技术.
- 评估疾病预测任务的分类性能.
主要成果:
- 当在一个扫描仪制造商的数据上训练的模型在不同制造商的数据上进行测试时,观察到分类性能显著下降.
- 基于ComBat的协调并没有对协调的多中心MRI数据的疾病分类性能带来明显的改善.
结论:
- 扫描器供应商效应在多中心神经成像研究中对深度学习模型的性能构成重大挑战.
- 目前基于ComBat的协调方法可能不足以克服由3D结构MRI中的扫描器变异引起的领域转移问题,用于疾病分类.
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