Xin Zhang
Learning developmental path signature features with deep learning framework for infant cognitive scores prediction
Abstract
Path signature has unique advantages on extracting high-order differential features of sequential data. Our team has been studying the path signature theory and actively applied it to various applications, including infant cognitive score prediction, human motion recognition, hand-written character recognition, hand-written text line recognition and writer identification etc. In this talk, I will share our most recent works on infant cognitive score prediction using deep path signature. The cognitive score can reveal individual’s abilities on intelligence, motion, language abilities. Recent research discovered that the cognitive ability is closely related with individual’s cortical structure and its development. We have proposed two frameworks to predict the cognitive score with different path signature features. For the first framework, we construct the temporal path signature along the age growth and extract signature features of developmental infant cortical features. By incorporating the cortical path signature into the multi-stream deep learning model, the individual cognitive score can be predicted with missing data issues. For the second framework, we propose deep path signature algorithm to compute the developmental feature and obtain the developmental connectivity matrix. Then we have designed the graph convolutional network for the score prediction. These two frameworks have been tested on two in-house cognitive data sets and reached the state-of-the-art results.
Our speaker
Xin Zhang received her Bachelor's degree in automatic engineering from Northwestern Polytechnical University in 2003, and the Master's and PhD degree in electrical engineering from Oklahoma State University, US, in 2005 and 2011 separately. She has been visiting scholar in University of North Carolina at Chapel Hill from 2018 to 2020. Currently, she is Associate Professor in the School of Electronic and Information Engineering, South China University of Technology (SCUT). Her research interests include computer vision, machine learning and medical image analysis. Dr. Zhang has published over 30 articles in journals, books, and conferences, including IEEE Trans., AAAI, MICCAI, and ACM MM. She has been the principal investigator of several research projects funded by NSFC Foundation, Ministry of Education, Guangdong NSF, and Microsoft Research Asia. She has also severed as the reviewer for many international conferences and journals.
