Skip to main content

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

  • 1274 Accesses

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 856)


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.


  • Variational autoencoder
  • Biliary atresia
  • CNN

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions


  1. Hou, X., Shen, L., Sun, K., et al.: Deep feature consistent variational autoencoder. In: Applications of Computer Vision, pp. 1133–1141. IEEE (2017)

    Google Scholar 

  2. Pu, Y., Gan, Z., Henao, R., et al.: Variational autoencoder for deep learning of images, labels and captions (2016)

    Google Scholar 

  3. Hsiao, C.H., Chang, M.H., Chen, H.L., et al.: Universal screening for biliary atresia using an infant stool color card in Taiwan. Hepatology 47(4), 1233–1240 (2008)

    CrossRef  Google Scholar 

  4. Chen, S.M., Chang, M.H., Du, J.C., et al.: Screening for biliary atresia by infant stool color card in Taiwan. Pediatrics 117(4), 1147 (2006)

    CrossRef  Google Scholar 

  5. Logan, S., Stanton, A.: Screening for biliary atresia. Lancet 342(8874), 1–9 (1993)

    Google Scholar 

  6. Dilokthanakul, N., Mediano, P.A.M., Garnelo, M., et al.: Deep unsupervised clustering with gaussian mixture variational autoencoders (2016)

    Google Scholar 

  7. Santara, A., Maji, D., Tejas, D.P., et al.: Faster learning of deep stacked autoencoders on multi-core systems using synchronized layer-wise pre-training (2016)

    Google Scholar 

  8. Semeniuta, S., Severyn, A., Barth, E.: A hybrid convolutional variational autoencoder for text generation (2017)

    Google Scholar 

Download references


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).

Author information

Authors and Affiliations


Corresponding author

Correspondence to Ling Li .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wan, Z., Qin, W., Song, K., Wang, B., Zhang, D., Li, L. (2019). Semi-Supervised Representation Learning for Infants Biliary Atresia Screening Using Deep CNN-Based Variational Autoencoder. In: Deng, K., Yu, Z., Patnaik, S., Wang, J. (eds) Recent Developments in Mechatronics and Intelligent Robotics. ICMIR 2018. Advances in Intelligent Systems and Computing, vol 856. Springer, Cham.

Download citation