Interpreting Deep Learning based Cerebral Palsy Prediction with Channel Attention

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

1 Scopus Citations
View graph of relations

Author(s)

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publication2021 BHI CONFERENCE PROCEEDINGS - 2021 IEEE EMBS International Conference on Biomedical and Health Informatics
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Electronic)9781665403580
ISBN (Print)9781665447706
Publication statusPublished - 2021

Publication series

NameBHI - IEEE EMBS International Conference on Biomedical and Health Informatics, Proceedings
ISSN (Print)2641-3590
ISSN (Electronic)2641-3604

Conference

Title2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI 2021)
LocationVirtual
PlaceGreece
CityAthens
Period27 - 30 July 2021

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.

Research Area(s)

  • Artificial neural network, Cerebral palsy, Channel attention, Deep learning

Citation Format(s)

Interpreting Deep Learning based Cerebral Palsy Prediction with Channel Attention. / Zhu, Manli; Men, Qianhui; Ho, Edmond S. L.; Leung, Howard; Shum, Hubert P. H.

2021 BHI CONFERENCE PROCEEDINGS - 2021 IEEE EMBS International Conference on Biomedical and Health Informatics. Institute of Electrical and Electronics Engineers Inc., 2021. (BHI - IEEE EMBS International Conference on Biomedical and Health Informatics, Proceedings).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review