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Resource Optimized Neural Architecture Search for 3D Medical Image Segmentation

  • Woong Bae
  • Seungho Lee
  • Yeha Lee
  • Beomhee Park
  • Minki Chung
  • Kyu-Hwan JungEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11765)

Abstract

Neural Architecture Search (NAS), a framework which automates the task of designing neural networks, has recently been actively studied in the field of deep learning. However, there are only a few NAS methods suitable for 3D medical image segmentation. Medical 3D images are generally very large; thus it is difficult to apply previous NAS methods due to their GPU computational burden and long training time. We propose the resource-optimized neural architecture search method which can be applied to 3D medical segmentation tasks in a short training time (1.39 days for 1 GB dataset) using a small amount of computation power (one RTX 2080Ti, 10.8 GB GPU memory). Excellent performance can also be achieved without retraining (fine-tuning) which is essential in most NAS methods. These advantages can be achieved by using a reinforcement learning-based controller with parameter sharing and focusing on the optimal search space configuration of macro search rather than micro search. Our experiments demonstrate that the proposed NAS method outperforms manually designed networks with state-of-the-art performance in 3D medical image segmentation.

Keywords

3D medical image segmentation AutoML Neural Architecture Search (NAS) Convolutional Neural Networks (CNN) 

Notes

Acknowledgement

This research was supported by a grant of the Korea Health Technology R&D Project(grant number: HI18C0673) through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea and Industrial Strategic technology development program (grant number: 10072064) funded by the Ministry of Trade Industry and Energy, Republic of Korea.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Woong Bae
    • 1
  • Seungho Lee
    • 1
  • Yeha Lee
    • 1
  • Beomhee Park
    • 1
  • Minki Chung
    • 1
  • Kyu-Hwan Jung
    • 1
    Email author
  1. 1.VUNO Inc.SeoulSouth Korea

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