Pak Lun Kevin Ding1, Riti Paul1, Baoxin Li1, Ameet C. Patel2, and Yuxiang Zhou2
1CIDSE, Arizona State University, Tempe, AZ, United States, 2RADIOLOGY, Mayo Clinic College of Medicine, Tempe, AZ, United States
1CIDSE, Arizona State University, Tempe, AZ, United States, 2RADIOLOGY, Mayo Clinic College of Medicine, Tempe, AZ, United States
In this paper, we propose a new network architecture - Variational Feedback Network (VFN) for fMRI reconstruction. The experimental results have demonstrated that, our proposed VFN outperforms other state-of-the-art methods. The performance also improves with greater number of folds.
Figure 1: The illustration of our feedback mechanism. Similar to RNN, the output of the network is used as an input to the network in the next fold.
Figure 2: An illustration of our feedback block. In the figure, green thick arrows represents $$$1 \times 1$$$ convolutional layers; Blue thick arrows denote $$$3 \times 3$$$ convolutional layers, each of them is followed by a normalization layer and a nonlinear activation layer; Red and yellow thick arrows represent the pooling layers and unpooling layers respectively; The skip connections are represented by the green thin arrows; The black thin arrows are the input/output for the feedback block, while the red thin arrows represent the input/output for the feedback connections.