Interpreting Deep Learning based Cerebral Palsy Prediction with Channel Attention

Zhu, Manli; Men, Qianhui; Ho, Edmond S.L.; Leung, Howard; Shum, Hubert P.H.

doi:10.1109/bhi50953.2021.9508619

Interpreting Deep Learning based Cerebral Palsy Prediction with Channel Attention

Zhu, Manli; Men, Qianhui; Ho, Edmond S.L.; Leung, Howard; Shum, Hubert P.H.

Authors

Manli Zhu

Qianhui Men

Edmond S.L. Ho

Howard Leung

Dr Hubert Shum hubert.shum@durham.ac.uk
Associate Professor

Abstract

Early prediction of cerebral palsy is essential as it leads to early treatment and monitoring. Deep learning has shown promising results in biomedical engineering thanks to its capacity of modelling complicated data with its non-linear architecture. However, due to their complex structure, deep learning models are generally not interpretable by humans, making it difficult for clinicians to rely on the findings. In this paper, we propose a channel attention module for deep learning models to predict cerebral palsy from infants’ body movements, which highlights the key features (i.e. body joints) the model identifies as important, thereby indicating why certain diagnostic results are found. To highlight the capacity of the deep network in modelling input features, we utilize raw joint positions instead of hand-crafted features. We validate our system with a real-world infant movement dataset. Our proposed channel attention module enables the visualization of the vital joints to this disease that the network considers. Our system achieves 91.67% accuracy, suppressing other state-of-the-art deep learning methods.

Citation

Zhu, M., Men, Q., Ho, E. S., Leung, H., & Shum, H. P. (2021). Interpreting Deep Learning based Cerebral Palsy Prediction with Channel Attention. . https://doi.org/10.1109/bhi50953.2021.9508619

Conference Name	2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)
Conference Location	Athens, Greece
Start Date	Jul 27, 2021
End Date	Jul 30, 2021
Acceptance Date	Jun 8, 2021
Online Publication Date	Aug 10, 2021
Publication Date	2021
Deposit Date	Jun 10, 2021
Publicly Available Date	Jun 10, 2021
Series ISSN	2641-3604,2641-3590
DOI	https://doi.org/10.1109/bhi50953.2021.9508619

Files

Accepted Conference Proceeding (8.8 Mb)
PDF

Copyright Statement
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.