Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (10): 183-190.doi: 10.11896/jsjkx.210800052

• Computer Graphics& Multimedia • Previous Articles     Next Articles

Neural Architecture Search for Light-weight Medical Image Segmentation Network

ZHANG Fu-chang, ZHONG Guo-qiang, MAO Yu-xu   

  1. College of Information Science and Engineering,Ocean University of China,Qingdao,Shandong 266100,China
  • Received:2021-08-05 Revised:2022-03-11 Online:2022-10-15 Published:2022-10-13
  • About author:ZHANG Fu-chang,born in 1996,postgraduate.His main research interests include neural architecture search and image processing.
    ZHONG Guo-qiang,born in 1981,professor,Ph.D supervisor,is a senior member of China Computer Federation.His main research interests include pattern recognition,machine learning and computer vision.
  • Supported by:
    National Key Research and Development Program of China(2018AAA0100400),Joint Fund of the Equipments Pre-Research and Ministry of Education of China(6141A020337),Natural Science Foundation of Shandong Province(ZR2020MF131),Open Fund of Engineering Research Center for Medical Data Mining and Application of Fujian Province(MDM2018007) and Science and Technology Program of Qingdao(21-1-4-ny-19-nsh).

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Abstract: Most of the existing medical image segmentation models with excellent performance are manually designed by domain experts.The design process usually requires a lot of professional knowledge and repeated experiments.In addition,the over complex segmentation model not only has high requirements for hardware resources,but also has low segmentation efficiency.An neural architecture search method named Auto-LW-MISN(Automatically Light-weight Medical Image Segmentation Network) is proposed for automatic construction of light-weight medical image segmentation network.In this paper,by constructing a light-weight search space,designing a search super network for medical image segmentation,and designing a differentiable search stra-tegy with complexity constraints,a neural architecture search framework for automatic search of light-weight medical image segmentation network is established.Experimental results on microscope cell images,liver CT images and prostate MR images show that Auto-LW-MISN can automatically construct light-weight segmentation models for different modes of medical images,and its segmentation accuracy is improved compared with U-net,Attention U-net,Unet+＋and NAS-Unet.

Key words: Deep learning, Differentiable neural architecture search, Light-weight convolutional neural networks, Automatic network architecture design, Medical image segmentation

CLC Number: 

  • TP391
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