Interpreting Deep Learning based Cerebral Palsy Prediction with Channel Attention

Manli Zhu, Qianhui Men, Edmond S. L. Ho, Howard Leung, Hubert Shum*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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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.
Original languageEnglish
Title of host publication2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)
Place of PublicationPiscataway
PublisherIEEE
Number of pages4
ISBN (Electronic)9781665447706
ISBN (Print)9781665403580
DOIs
Publication statusPublished - 27 Jul 2021
EventIEEE International Conference on Biomedical and Health Informatics (BHI) : Reshaping healthcare through advanced AI-enabled health informatics for a better quality of life - Virtual
Duration: 27 Jul 202130 Jul 2021
https://www.bhi-bsn-2021.org/?page_id=2336

Publication series

NameIEEE EMBS International Conference on Biomedical and Health Informatics
PublisherIEEE
ISSN (Print)2641-3590
ISSN (Electronic)2641-3604

Conference

ConferenceIEEE International Conference on Biomedical and Health Informatics (BHI)
Abbreviated titleIEEE BHI 2021
Period27/07/2130/07/21
Internet address

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