SYSTEM, METHOD AND APPARATUS FOR OBTAINING SENSITIVE AND SPECIFIC PREDICTIONS FROM DEEP NEURAL NETWORKS
The present disclosure relates to a novel system, method and apparatus for encoding activations generated by a generic deep neural network from an image that can be used in conjunction with memory storage devices for highly sensitive and specific prediction of labels associated images of general objects, scenes or persons. The method and apparatus may involve the use of a processor circuit, for example an application server, for automatically predicting a plurality of labels (e.g., diagnostic indices) from an input image and non-image descriptive information associated with the image, where non-image information may include general descriptors of image content (e.g., ear, chest, skin, face), acquisition modality (e.g., hyperspectral image, magnetic resonance image, x-ray, computed tomography, ultrasound, acquisition parameters) and specific descriptors (e.g., unique name or ID, sex, ethnicity, age, date, disease status). A plurality of image feature vectors are extracted from the spatial bottleneck layer from one or more trained convolutional neural networks. Features are stored in memory along with non-image descriptive data. Features extracted from a new image are matched to features stored in memory based on feature-to-feature distance and constrained by non-image information, in order to predict unknown labels. Where features extracted from images of a specific object, scene or person have been previously stored in memory, features extracted from a new image may be used to form a differential prediction based on previous data.