Deep learning is a form of machine learning using a convolutional neural network architecture that shows tremendous promise for imaging applications. It is increasingly being adapted from its original demonstration in computer vision applications to medical imaging.
SUMMARY
Example of a simple deep network architecture. The goal of this network is to classify MR images into 4 specific diagnoses (normal, tumor, stroke, hemorrhage). Multiple different images form the training set. For each new case, the image is broken down into its constituent voxels, each one of which acts as an input into the network. This example has 3 hidden layers with 7 neurons in each layer, and the final output is the probabilities of the 4 classification states. All layers are fully connected. At the bottom is a zoomed-in view of an individual neuron in the second hidden layer, which receives input from the previous layer, performs a standard matrix multiplication (including a bias term), passes this through a nonlinear function (the rectified linear unit function in this example), and outputs a single value to all the neurons of the next layer.
Because of the high volume and wealth of multimodal imaging information acquired in typical studies, neuroradiology is poised to be an early adopter of deep learning. Compelling deep learning research applications have been demonstrated, and their use is likely to grow rapidly. This review article describes the reasons, outlines the basic methods used to train and test deep learning models, and presents a brief overview of current and potential clinical applications with an emphasis on how they are likely to change future neuroradiology practice. Facility with these methods among neuroimaging researchers and clinicians will be important to channel and harness the vast potential of this new method.
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Deep learning is a form of machine learning using a convolutional neural network architecture that shows tremendous promise for imaging applications. It is increasingly being adapted from its original demonstration in computer vision applications to medical imaging.