Siamese Verification Framework for Autism Identification During Infancy Using Cortical Path Signature Features
| Autor: | Xinyao Ding, Jing Xia, Lufan Liao, Xiangmin Xu, Xin Zhang, Zhengwang Wu, Gang Li, Li Wang, Hao Ni |
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| Rok vydání: | 2020 |
| Předmět: |
Training set
Computer science business.industry Pattern recognition 02 engineering and technology medicine.disease Article Signature (logic) 03 medical and health sciences Identification (information) 0302 clinical medicine Neuroimaging Autism spectrum disorder Path (graph theory) 0202 electrical engineering electronic engineering information engineering medicine Autism 020201 artificial intelligence & image processing Sensitivity (control systems) Artificial intelligence business 030217 neurology & neurosurgery |
| Zdroj: | ISBI Proc IEEE Int Symp Biomed Imaging |
| DOI: | 10.1109/isbi45749.2020.9098385 |
| Popis: | Autism spectrum disorder (ASD) is a complex neurodevelopmental disability, which is lack of biologic diagnostic markers. Therefore, exploring the ASD Identification directly from brain imaging data has been an important topic. In this work, we propose the Siamese verification model to identify ASD using 6 and 12 months cortical features. Rather than directly classifying a testing subject is ASD or not, we determine whether it has the same or different label with the reference subject who has been successfully diagnosed. Then, based on the comparison to all the reference subjects, we can predict the label of the testing subject. The advantage of modeling the classification problem as a verification framework is that it can greatly enlarge the training data size and enable us to train a more accurate and reliable model in an end-to-end manner. In addition, to further improve the classification performance, we introduce the path signature (PS) features, which can capture the dynamic longitudinal information of the brain development for the ASD Identification. Experiments showed that our proposed method reaches the best result, i.e., 87% accuracy, 83% sensitivity and 90% specificity comparing to the state-of-the-art methods. |
| Databáze: | OpenAIRE |
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