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Semi-Supervised Representation Learning for Infants Biliary Atresia Screening Using Deep CNN-Based Variational Autoencoder

  • Zhiyong Wan
  • Wenjian Qin
  • Kuiliang Song
  • Bin Wang
  • Dahao Zhang
  • Ling Li
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 856)

Abstract

The challenge of convolutional networks (CNNs) for medical imaging analysis is to train the network model with limited well labeled dataset. Since a variational autoencoder (VAE) is able to learn the probability distribution on data for describing an observation in terms of its latent attributes by unsupervised manner, it has emerged as one of the most popular unsupervised learning technology in computer vision applications. In this paper, we presented a semi-supervised representation learning approach for screening infant’s biliary atresia using convolutional variational autoencoder (CVAE). Firstly, we leveraged a smartphone’s camera for infant’s stool images collection. Secondly, a pre-trained deep convolutional variation autoencoder was used to train the feature extractor for the infant’s stool image-features extraction, and finally we fine-tuned the last classification layers for identifying acholic from normal stools. We compared our screening approach with “tradition stool color card” method; the results demonstrated CVAE model has a higher accuracy rate 92.16%. Universal screening biliary atresia by semi-supervised representation learning may be a valuable technology to help parents identify acholic stools in the perinatal period, which may ultimately lead to improved native liver survival probabilities.

Keywords

Variational autoencoder Biliary atresia CNN 

Notes

Acknowledgments

This study was financed partially by the National Natural Science Foundation of China (NSFC: 61501444), Shenzhen Technology Development Project Fund under Grant JSGG20160429192140681.

Guangdong province science and technology plan projects (Grant No.2015B020233004), Shenzhen basic technology research project (Grant No. JCYJ20160429174611494, JCYJ20170818160306270).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Zhiyong Wan
    • 1
  • Wenjian Qin
    • 1
  • Kuiliang Song
    • 2
  • Bin Wang
    • 3
  • Dahao Zhang
    • 3
  • Ling Li
    • 1
  1. 1.Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesBeijingChina
  2. 2.Shandong Zhongbaokang Medical Implements Co. LTDZiboChina
  3. 3.ShenZhen Children’s Hospital ChinaShenzhenChina

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