TY - GEN
T1 - Optimize CNN Model for FMRI Signal Classification Via Adanet-Based Neural Architecture Search
AU - Dai, Haixing
AU - Ge, Fangfei
AU - Li, Qing
AU - Zhang, Wei
AU - Liu, Tianming
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/4
Y1 - 2020/4
N2 - Recent studies showed that convolutional neural network (CNN) models possess remarkable capability of differentiating and characterizing fMRI signals from cortical gyri and sulci. In addition, visualization and analysis of the filters in the learned CNN models suggest that sulcal fMRI signals are more diverse and have higher frequency than gyral signals. However, it is not clear whether the gyral fMRI signals can be further divided into sub-populations, e.g., 3-hinge areas vs 2-hinge areas. It is also unclear whether the CNN models of two classes (gyral vs sulcal) classification can be further optimized for three classes (3-hinge gyral vs 2-hinge gyral vs sulcal) classification. To answer these questions, in this paper, we employed the AdaNet framework to design a neural architecture search (NAS) system for optimizing CNN models for three classes fMRI signal classification. The core idea is that AdaNet adaptively learns both the optimal structure of the CNN network and its weights so that the learnt CNN model can effectively extract discriminative features that maximize the classification accuracies of three classes of 3-hinge gyral, 2-hinge gyral and sulcal fMRI signals. We evaluated our framework on the Autism Brain Imaging Data Exchange (ABIDE) dataset, and experiments showed that our framework can obtained significantly better results, in terms of both classification accuracy and extracted features.
AB - Recent studies showed that convolutional neural network (CNN) models possess remarkable capability of differentiating and characterizing fMRI signals from cortical gyri and sulci. In addition, visualization and analysis of the filters in the learned CNN models suggest that sulcal fMRI signals are more diverse and have higher frequency than gyral signals. However, it is not clear whether the gyral fMRI signals can be further divided into sub-populations, e.g., 3-hinge areas vs 2-hinge areas. It is also unclear whether the CNN models of two classes (gyral vs sulcal) classification can be further optimized for three classes (3-hinge gyral vs 2-hinge gyral vs sulcal) classification. To answer these questions, in this paper, we employed the AdaNet framework to design a neural architecture search (NAS) system for optimizing CNN models for three classes fMRI signal classification. The core idea is that AdaNet adaptively learns both the optimal structure of the CNN network and its weights so that the learnt CNN model can effectively extract discriminative features that maximize the classification accuracies of three classes of 3-hinge gyral, 2-hinge gyral and sulcal fMRI signals. We evaluated our framework on the Autism Brain Imaging Data Exchange (ABIDE) dataset, and experiments showed that our framework can obtained significantly better results, in terms of both classification accuracy and extracted features.
KW - 2-hinge
KW - 3-hinge
KW - Auto ML
KW - CNN
KW - Neural Architecture Search
UR - http://www.scopus.com/inward/record.url?scp=85085855923&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85085855923&partnerID=8YFLogxK
U2 - 10.1109/ISBI45749.2020.9098574
DO - 10.1109/ISBI45749.2020.9098574
M3 - Conference contribution
AN - SCOPUS:85085855923
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 1399
EP - 1403
BT - ISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging
PB - IEEE Computer Society
T2 - 17th IEEE International Symposium on Biomedical Imaging, ISBI 2020
Y2 - 3 April 2020 through 7 April 2020
ER -