TY - GEN
T1 - Early diagnosis of autism disease by multi-channel CNNs
AU - Li, Guannan
AU - Liu, Mingxia
AU - Sun, Quansen
AU - Shen, Dinggang
AU - Wang, Li
N1 - Funding Information:
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.
Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Autism
KW - Convolutional neural network
KW - Deep multi-instance learning
KW - Early diagnosis
UR - http://www.scopus.com/inward/record.url?scp=85054510833&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-00919-9_35
DO - 10.1007/978-3-030-00919-9_35
M3 - Conference contribution
AN - SCOPUS:85054510833
SN - 9783030009182
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 303
EP - 309
BT - Machine Learning in Medical Imaging - 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Proceedings
A2 - Liu, Mingxia
A2 - Suk, Heung-Il
A2 - Shi, Yinghuan
PB - Springer Verlag
T2 - 9th International Workshop on Machine Learning in Medical Imaging, MLMI 2018 held in conjunction with the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018
Y2 - 16 September 2018 through 16 September 2018
ER -