  Resting state fMRI is an imaging modality which reveals brain activity
localization through signal changes, in what is known as Resting State Networks
(RSNs). This technique is gaining popularity in neurosurgical pre-planning to
visualize the functional regions and assess regional activity. Labeling of
rs-fMRI networks require subject-matter expertise and is time consuming,
creating a need for an automated classification algorithm. While the impact of
AI in medical diagnosis has shown great progress; deploying and maintaining
these in a clinical setting is an unmet need. We propose an end-to-end
reproducible pipeline which incorporates image processing of rs-fMRI in a
cloud-based workflow while using deep learning to automate the classification
of RSNs. We have architected a reproducible Azure Machine Learning cloud-based
medical imaging concept pipeline for fMRI analysis integrating the popular
FMRIB Software Library (FSL) toolkit. To demonstrate a clinical application
using a large dataset, we compare three neural network architectures for
classification of deeper RSNs derived from processed rs-fMRI. The three
algorithms are: an MLP, a 2D projection-based CNN, and a fully 3D CNN
classification networks. Each of the net-works was trained on the rs-fMRI
back-projected independent components giving >98% accuracy for each
classification method.
