Event Details
Denoising Task-Based FMRI Data Using Hybrid Deep Neural Network Model
Presenter: Sahar Kashfolayat
Supervisor:
Date: Fri, June 17, 2022
Time: 15:00:00 - 16:00:00
Place: via Zoom - please see link below
ABSTRACT
Zoom link: https://uvic.zoom.us/j/6753460454?pwd=bnVLcWN3U1BBYkJWYm5aWlFaWjBwZz09
Meeting ID: 675 346 0454
Password: 348659
Abstract: Error management is one of the most important concerns in medical science. A doctor's ability to make an accurate diagnosis can save a patient's life. Therefore, it is essential that doctors have access to accurate medical data in order to make the best possible decisions for their patients. This means that technology can help doctors be more accurate in their diagnoses and reduce the number of mistakes they make. In this study, a hybrid deep neural network (HDNN) is proposed to reduce the noise in task-based fMRI data. The proposed model has been developed to improve the performance of the DNN model. This network, like the DNN model, has a sequential structure. The model uses three common layers and four non-common layers to perform noise reduction. The first three layers consist of two one-dimensional convolutional filters to reduce physiological noise in both GM and non-GM and one LSTM layer to consider the temporal correlation of input data. The final layers are used to separate and select data, including a fully connected and a conventional selective layer for GM and non-GM input data.