GLACIER: GLASS-BOX TRANSFORMER FOR INTERPRETABLE DYNAMIC NEUROIMAGING.
GLACIER: GLASS-BOX TRANSFORMER FOR INTERPRETABLE DYNAMIC NEUROIMAGING.
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Glacier：用于可解释动态神经成像的玻璃盒变压器。
DOI：
10.1109/icassp49357.2023.10097126
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Plis,Sergey
中科院分区：
文献类型：
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作者：
Mahmood,Usman;Fu,Zening;Calhoun,Vince;Plis,Sergey
深度学习模型可以在许多任务中表现得与人类一样好或更好，特别是与视觉相关的任务。这些模型几乎专门用于执行分类或预测。然而，深度学习模型通常具有黑盒性质，并且通常难以解释模型或特征。缺乏可解释性导致了将深度学习应用于神经成像等领域的限制，这些领域的结果必须是透明和可解释的。因此，我们提出了一个“玻璃盒”深度学习模型，并将其应用于神经成像领域。我们的模型连续混合了空间和时间维度，以估计大脑内在网络之间的动态连接。我们的模型产生的可解释的连接矩阵导致使用多个功能MRI数据集在许多任务上击败最先进的模型。更重要的是，我们的模型估计基于任务的灵活连接矩阵，不像静态方法，如皮尔逊的相关系数。
Deep learning models can perform as well or better than humans in many tasks, especially vision related. Almost exclusively, these models are used to perform classification or prediction. However, deep learning models are usually of black-box nature, and it is often difficult to interpret the model or the features. The lack of interpretability causes a restrain from applying deep learning to fields such as neuroimaging, where the results must be transparent, and interpretable. Therefore, we present a ’glass-box’ deep learning model and apply it to the field of neuroimaging. Our model mixes spatial and temporal dimensions in succession to estimate dynamic connectivity between the brain’s intrinsic networks. The interpretable connectivity matrices produced by our model result in beating state-of-the-art models on many tasks using multiple functional MRI datasets. More importantly, our model estimates task-based flexible connectivity matrices, unlike static methods such as Pearson’s correlation coefficients.