Interpreting Deep Learning based Cerebral Palsy Prediction with Channel Attention

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

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, July). Interpreting Deep Learning based Cerebral Palsy Prediction with Channel Attention. Presented at 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), Athens, Greece

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)
• 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
• Public URL: https://durham-repository.worktribe.com/output/1139443