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.
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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 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-pp.ucl.ac.uk/id/eprint/10102952 |
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