Figure 1.

Schematic of a deep neural network for imaging data. The input data often is down-sampled to a fixed height and width and nonimaging related data removed to prevent information about the target label from entering the training data. The input image data is represented to the algorithm as a fixed-size matrix corresponding to the image's pixel intensities in standard color channels (ie, red, green, blue). Each matrix (or set of matrices for a color image) represents an image that is fed into the first layer of the neural network (blue nodes). Subsequent layers of the neural network (yellow nodes) can be customized to perform a wide range of functions to the input matrix, transforming the data in a highly flexible way before feeding the data to the next layer of nodes. Deeper layers of nodes tend to learn complex interactions and higher level “features” derived from the input matrix. Each node in each layer of the network adjusts its weights during training, and the entire network is trained using the provided output labels in the training data. Different neural network architectures can be used to achieve object detection, disease classification or segmentation. HCM: hypertrophic cardiomyopathy; PAH: pulmonary arterial hypertension