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Towards a Deeper Insight Into Face Detection in Neonatal Wards

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 (MICCAI 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15005))

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Abstract

Neonatal face detection is the prerequisite for face-based intelligent medical applications. Nevertheless, it has been found that this area has received minimal attention in existing research. The paucity of open-source, large-scale datasets significantly constrains current studies, which are further compounded by issues such as large-scale occlusions, class imbalance, and precise localization requirements. This work aims to address these challenges from both data and methodological perspectives. We constructed the first open-source face detection dataset for neonates, involving images from 1,000 neonates in the neonatal wards. Utilizing this dataset and adopting NICUface-RF as the baseline, we introduce two novel modules. The hierarchical contextual classification aims to improve the positive/negative anchor ratios and alleviate large-scale occlusions. Concurrently, the DIoU-aware NMS is designed to preserve bounding boxes of superior localization quality by employing predicted DIoUs as the ranking criterion in NMS procedures. Experimental results illustrate the superiority of our method. The dataset and code is available at https://github.com/neonatal-pain.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (62306272 and 62276237), the National Key Research and Development Program of China (2023YFC3603102), the Pioneer and “Leading Goose” Research and Development Program of Zhejiang (2024C03027).

Author information

Authors and Affiliations

  1. College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China

    Yisheng Zhao, Huaiyu Zhu & Yun Pan

  2. Nursing Department, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, China

    Qi Shu

  3. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China

    Ruohong Huan

  4. Hospital Infection-Control Department, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, China

    Shuohui Chen

Authors
  1. Yisheng Zhao
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  2. Huaiyu Zhu
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  3. Qi Shu
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  4. Ruohong Huan
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  5. Shuohui Chen
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  6. Yun Pan
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Corresponding authors

Correspondence to Huaiyu Zhu or Yun Pan .

Editor information

Editors and Affiliations

  1. Children’s National Hospital/George Washington University, Washington, DC, USA

    Marius George Linguraru

  2. The Chinese University of Hong Kong, Hong Kong, China

    Qi Dou

  3. Technical University of Denmark, Kgs Lyngby, Denmark

    Aasa Feragen

  4. Imperial College London, London, UK

    Stamatia Giannarou

  5. Imperial College London, London, UK

    Ben Glocker

  6. Universitat de Barcelona, Barcelona, Spain

    Karim Lekadir

  7. Helmholtz Munich, Technical University of Munich and King’s College London, Munich, Germany

    Julia A. Schnabel

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Zhao, Y., Zhu, H., Shu, Q., Huan, R., Chen, S., Pan, Y. (2024). Towards a Deeper Insight Into Face Detection in Neonatal Wards. In: Linguraru, M.G., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15005. Springer, Cham. https://doi.org/10.1007/978-3-031-72086-4_66

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  • DOI: https://doi.org/10.1007/978-3-031-72086-4_66

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-72085-7

  • Online ISBN: 978-3-031-72086-4

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