Early Diagnosis of Autism Disease by Multi-channel CNNs

  • Guannan Li
  • Mingxia Liu
  • Quansen Sun
  • Dinggang ShenEmail author
  • Li WangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11046)


Currently there are still no early biomarkers to detect infants with risk of autism spectrum disorder (ASD), which is mainly diagnosed based on behavior observations at three or four years old. Since intervention efforts may miss a critical developmental window after 2 years old, it is significant to identify imaging-based biomarkers for early diagnosis of ASD. Although some methods using magnetic resonance imaging (MRI) for brain disease prediction have been proposed in the last decade, few of them were developed for predicting ASD in early age. Inspired by deep multi-instance learning, in this paper, we propose a patch-level data-expanding strategy for multi-channel convolutional neural networks to automatically identify infants with risk of ASD in early age. Experiments were conducted on the National Database for Autism Research (NDAR), with results showing that our proposed method can significantly improve the performance of early diagnosis of ASD.


Autism Convolutional neural network Early diagnosis Deep multi-instance learning 



Data used in the preparation of this manuscript were obtained from the NIH-supported National Database for Autism Research (NDAR). NDAR is a collaborative informatics system created by the National Institutes of Health to provide a national resource to support and accelerate research in autism. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or of the Submitters submitting original data to NDAR.

This work was supported in part by National Institutes of Health grants MH109773, MH100217, MH070890, EB006733, EB008374, EB009634, AG041721, AG042599, MH088520, MH108914, MH107815, and MH113255.


  1. 1.
    Newschaffer, C.J., et al.: The epidemiology of autism spectrum disorders. Dev. Disab. Res. Rev. 8, 151–161 (2002)Google Scholar
  2. 2.
    Filipek, P.A., et al.: The screening and diagnosis of autistic spectrum disorders. J. Autism Dev. Disorders 29, 439–484 (1999)Google Scholar
  3. 3.
    Baird, G., Cass, H., Slonims, V.: Diagnosis of autism. BMJ Brit. Med. J. 327, 488–493 (2003)CrossRefGoogle Scholar
  4. 4.
    Chen, R., Jiao, Y., Herskovits, E.H.: Structural MRI in autism spectrum disorder. Pediatr. Res. 69, 63R (2011)CrossRefGoogle Scholar
  5. 5.
    Schumann, C.M., et al.: The amygdala is enlarged in children but not adolescents with autism; the hippocampus is enlarged at all ages. J. Neurosci. 24, 6392 (2004)CrossRefGoogle Scholar
  6. 6.
    Greimel, E., et al.: Changes in grey matter development in autism spectrum disorder. Brain Struct. Funct 218, 929–942 (2013)CrossRefGoogle Scholar
  7. 7.
    Thakkar, K.N., et al.: Response monitoring, repetitive behaviour and anterior cingulate abnormalities in autism spectrum disorders (ASD). Brain 131, 2464–2478 (2008)CrossRefGoogle Scholar
  8. 8.
    Ciresan, D.C., Meier, U., Masci, J., Maria Gambardella, L., Schmidhuber, J.: Flexible, high performance convolutional neural networks for image classification. In: IJCAI Proceedings-International Joint Conference on Artificial Intelligence, pp. 1237. Barcelona, Spain, (2011)Google Scholar
  9. 9.
    Sarraf, S., Tofighi, G.: DeepAD: Alzheimer′ s Disease Classification via Deep Convolutional Neural Networks using MRI and fMRI. bioRxiv 070441 (2016)Google Scholar
  10. 10.
    LeCun, Y.: LeNet-5, convolutional neural networks (2015). http://yann/ Scholar
  11. 11.
    Zhong, Z., Jin, L., Xie, Z.: High performance offline handwritten chinese character recognition using googlenet and directional feature maps. In: 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 846–850. IEEE (2015)Google Scholar
  12. 12.
    Liu, M., Zhang, J., Adeli, E., Shen, D.: Landmark-based deep multi-instance learning for brain disease diagnosis. Med. Image Anal. 43, 157–168 (2018)CrossRefGoogle Scholar
  13. 13.
    Payakachat, N., Tilford, J.M., Ungar, W.J.: National Database for Autism Research (NDAR): Big data opportunities for health services research and health technology assessment. PharmacoEconomics 34, 127–138 (2016)CrossRefGoogle Scholar
  14. 14.
    Zhang, J., Gao, Y., Gao, Y., Munsell, B.C., Shen, D.: Detecting anatomical landmarks for fast Alzheimer’s disease diagnosis. IEEE Trans. Med. Imaging 35, 2524 (2016)CrossRefGoogle Scholar
  15. 15.
    Mardia, K.: Assessment of multinormality and the robustness of Hotelling’s T2 test. Appl. Stat., 163–171 (1975)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringNanjing University of Science and TechnologyNanjingChina
  2. 2.Department of Radiology and Biomedical Research Imaging CenterUniversity of North Carolina at Chapel HillChapel HillUSA

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