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Infant Cognitive Scores Prediction With Multi-stream Attention-based Temporal Path Signature Features

Ni, H; Zhang, X; Chen, J; Li, C; Xu, X; Wu, Z; Li, W; ... Li, G; + view all (2020) Infant Cognitive Scores Prediction With Multi-stream Attention-based Temporal Path Signature Features. In: Martel, A.L. and et al, , (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science. Springer: Cham. Green open access

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Abstract

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.

Type: Proceedings paper
Title: Infant Cognitive Scores Prediction With Multi-stream Attention-based Temporal Path Signature Features
Event: 23rd International conference on medical image computing and computer assisted intervention
Location: Lima, Peru
Dates: 04 October 2020 - 08 October 2020
ISBN: 978-3-030-59727-6
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-030-59728-3_14
Publisher version: https://doi.org/10.1007/978-3-030-59728-3_14
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Path signature feature, Infant brain, Cognitive ability
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Mathematics
URI: https://discovery.ucl.ac.uk/id/eprint/10102952
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