Abstract
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.
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References
Newschaffer, C.J., et al.: The epidemiology of autism spectrum disorders. Dev. Disab. Res. Rev. 8, 151–161 (2002)
Filipek, P.A., et al.: The screening and diagnosis of autistic spectrum disorders. J. Autism Dev. Disorders 29, 439–484 (1999)
Baird, G., Cass, H., Slonims, V.: Diagnosis of autism. BMJ Brit. Med. J. 327, 488–493 (2003)
Chen, R., Jiao, Y., Herskovits, E.H.: Structural MRI in autism spectrum disorder. Pediatr. Res. 69, 63R (2011)
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)
Greimel, E., et al.: Changes in grey matter development in autism spectrum disorder. Brain Struct. Funct 218, 929–942 (2013)
Thakkar, K.N., et al.: Response monitoring, repetitive behaviour and anterior cingulate abnormalities in autism spectrum disorders (ASD). Brain 131, 2464–2478 (2008)
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)
Sarraf, S., Tofighi, G.: DeepAD: Alzheimer′ s Disease Classification via Deep Convolutional Neural Networks using MRI and fMRI. bioRxiv 070441 (2016)
LeCun, Y.: LeNet-5, convolutional neural networks (2015). http://yann/.lecun.com/exdb/lenet20
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)
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)
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)
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)
Mardia, K.: Assessment of multinormality and the robustness of Hotelling’s T2 test. Appl. Stat., 163–171 (1975)
Acknowledgments
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.
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Li, G., Liu, M., Sun, Q., Shen, D., Wang, L. (2018). Early Diagnosis of Autism Disease by Multi-channel CNNs. In: Shi, Y., Suk, HI., Liu, M. (eds) Machine Learning in Medical Imaging. MLMI 2018. Lecture Notes in Computer Science(), vol 11046. Springer, Cham. https://doi.org/10.1007/978-3-030-00919-9_35
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DOI: https://doi.org/10.1007/978-3-030-00919-9_35
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