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Conditional Domain Adversarial Transfer for Robust Cross-Site ADHD Classification Using Functional MRI
Abstract
There is a growing number of large scale cross-site database collection of resting-state functional magnetic resonance imaging (rs-fMRI) for studying neurobehavioral diseases, such as ADHD. Although a large amount of data benefits machine learning-based classification methods, the idiosyncratic variability of each site can deteriorate cross-site generalization ability. This challenge creates a bottleneck in requiring a large number of labeled samples of each site. Hence in this research, we utilize an approach of conditional adversarial domain adaptation network (CDAN) to learn a discriminative fMRI representation that is site-invariant for unsupervised transfer of ADHD classification. We evaluate our framework on a multi-site ADHD dataset and achieve improvement in transferring between sites. Further visualization reveals that there indeed exists a substantial site discrepancy and statistically analysis indicates that male's rs-fMRI could be more vulnerable toward site-specific effects.
Figures
A schematic of our proposed DNN-based adversarial learning which could extract class conditioned and site-invariant features.
A schematic of our proposed DNN-based adversarial learning which could extract class conditioned and site-invariant features.
The t-SNE plot of the feature extracted by DNN an CDAN+E. Select K-NI and K-O to illustrate here. (red plus: objects from source sample set, tomato tri: TD objects from source sample set, black plus: objects with ADHD from target sample set, blue tri: TD objects from target sample set)
The t-SNE plot of the feature extracted by DNN an CDAN+E. Select K-NI and K-O to illustrate here. (red plus: objects from source sample set, tomato tri: TD objects from source sample set, black plus: objects with ADHD from target sample set, blue tri: TD objects from target sample set)
Keywords
ADHD | fMRI | adversarial domain adaptation | multi-site transfer
Authors
Publication Date
2020/05/04
Conference
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
DOI
10.1109/icassp40776.2020.9054606
Publisher
IEEE