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International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2019: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 pp 13–21Cite as

MVP-Net: Multi-view FPN with Position-Aware Attention for Deep Universal Lesion Detection

Part of the Lecture Notes in Computer Science book series (LNIP,volume 11769)

Abstract

Universal lesion detection (ULD) on computed tomography (CT) images is an important but underdeveloped problem. Recently, deep learning-based approaches have been proposed for ULD, aiming to learn representative features from annotated CT data. However, the hunger for data of deep learning models and the scarcity of medical annotation hinders these approaches to advance further. In this paper, we propose to incorporate domain knowledge in clinical practice into the model design of universal lesion detectors. Specifically, as radiologists tend to inspect multiple windows for an accurate diagnosis, we explicitly model this process and propose a multi-view feature pyramid network (FPN), where multi-view features are extracted from images rendered with varied window widths and window levels; to effectively combine this multi-view information, we further propose a position-aware attention module. With the proposed model design, the data-hunger problem is relieved as the learning task is made easier with the correctly induced clinical practice prior. We show promising results with the proposed model, achieving an absolute gain of \(\mathbf {5.65\%}\) (in the sensitivity of FPs@4.0) over the previous state-of-the-art on the NIH DeepLesion dataset.

Keywords

  • Universal lesion detection
  • Multi-view
  • Position-aware
  • Attention

Z. Li and S. Zhang—Equal contribution. This work is done when Zihao Li is an intern at Deepwise AI Lab.

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Fig. 1.
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Fig. 3.

Notes

  1. 1.

    Windowing, also known as gray-level mapping, is used to change the appearance of the picture to highlight particular structures.

  2. 2.

    As a common practice in machine learning, we refer to reconstruction under a certain window width and window level as a view of that CT.

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Acknowledgement

This work is funded by the National Natural Science Foundation of China (Grant No. 61876181, 61721004, 61403383, 61625201, 61527804) and the Projects of Chinese Academy of Sciences (Grant QYZDB-SSW-JSC006 and Grant 173211KYSB20160008). We would like to thank Feng Liu for valuable discussions.

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Authors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Zihao Li, Junge Zhang & Kaiqi Huang

  2. Deepwise AI Lab, Beijing, China

    Zihao Li, Yizhou Wang & Yizhou Yu

  3. Department of Computer Science, Peking University, Haidian District, China

    Shu Zhang & Yizhou Wang

  4. Peng Cheng Laboratory, Shenzhen, China

    Yizhou Wang

Authors
  1. Zihao Li
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  2. Shu Zhang
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  3. Junge Zhang
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  4. Kaiqi Huang
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  5. Yizhou Wang
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  6. Yizhou Yu
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Corresponding author

Correspondence to Yizhou Yu .

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Editors and Affiliations

  1. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Dinggang Shen

  2. University of Georgia, Athens, GA, USA

    Prof. Tianming Liu

  3. Western University, London, ON, Canada

    Dr. Terry M. Peters

  4. Yale University, New Haven, CT, USA

    Lawrence H. Staib

  5. University of Strasbourg, Illkirch, France

    Caroline Essert

  6. United Imaging Intelligence, Shanghai, China

    Sean Zhou

  7. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Prof. Dr. Pew-Thian Yap

  8. Western University, London, ON, Canada

    Ali Khan

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Li, Z., Zhang, S., Zhang, J., Huang, K., Wang, Y., Yu, Y. (2019). MVP-Net: Multi-view FPN with Position-Aware Attention for Deep Universal Lesion Detection. In: , et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11769. Springer, Cham. https://doi.org/10.1007/978-3-030-32226-7_2

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  • DOI: https://doi.org/10.1007/978-3-030-32226-7_2

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