Infant Cognitive Scores Prediction with Multi-stream Attention-Based Temporal Path Signature Features.
Infant Cognitive Scores Prediction with Multi-stream Attention-Based Temporal Path Signature Features.
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DOI：
10.1007/978-3-030-59728-3_14
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发表时间：
2020-10
期刊：
影响因子：
--
通讯作者：
Li G
中科院分区：
文献类型：
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作者：
Zhang X;Cheng J;Ni H;Li C;Xu X;Wu Z;Wang L;Lin W;Shen D;Li G
人类大脑在生命的第一年有惊人的快速发展。一些研究揭示了这一时期认知技能与大脑皮层形态之间的紧密联系。然而，由于纵向研究中存在样本量小、数据缺失等问题，利用脑形态学特征预测认知成绩仍是一个巨大的挑战。在这项工作中，第一次，我们引入了路径签名的方法来探索隐藏的纵向皮质形态特征的分析和几何属性。提出了一种新的BrainPSNet，其具有可区分的时间路径签名层，以产生不同时间点和各种时间颗粒的信息表示。此外，包括双流神经网络以将原始特征和路径签名特征的联合收割机组组合以用于预测认知得分。更重要的是，考虑到每个大脑区域对认知功能的不同影响，我们设计了一个基于学习的注意力模板生成器来自动加权区域。实验是在内部纵向数据集上进行的。通过与几种最新算法的比较，该方法达到了最先进的性能。分析了形态特征与认知能力的关系。
There is stunning rapid development of human brains in the first year of life. Some studies have revealed the tight connection between cognition skills and cortical morphology in this period. Nonetheless, it is still a great challenge to predict cognitive scores using brain morphological features, given issues like small sample size and missing data in longitudinal studies. In this work, for the first time, we introduce the path signature method to explore hidden analytical and geometric properties of longitudinal cortical morphology features. A novel BrainPSNet is proposed with a differentiable temporal path signature layer to produce informative representations of different time points and various temporal granules. Further, a two-stream neural network is included to combine groups of raw features and path signature features for predicting the cognitive score. More importantly, considering different influences of each brain region on the cognitive function, we design a learning-based attention mask generator to automatically weight regions correspondingly. Experiments are conducted on an in-house longitudinal dataset. By comparing with several recent algorithms, the proposed method achieves the state-of-the-art performance. The relationship between morphological features and cognitive abilities is also analyzed.