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.
Original language | English |
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Title of host publication | Machine Learning in Medical Imaging - 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Proceedings |
Editors | Mingxia Liu, Heung-Il Suk, Yinghuan Shi |
Publisher | Springer Verlag |
Pages | 303-309 |
Number of pages | 7 |
ISBN (Print) | 9783030009182 |
DOIs | |
Publication status | Published - 2018 |
Event | 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 - Granada, Spain Duration: 2018 Sept 16 → 2018 Sept 16 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 11046 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Other
Other | 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 |
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Country/Territory | Spain |
City | Granada |
Period | 18/9/16 → 18/9/16 |
Bibliographical note
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.
Keywords
- Autism
- Convolutional neural network
- Deep multi-instance learning
- Early diagnosis
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science