ARTIFICIAL NEURAL NETWORK COMBINING SENSORY SIGNAL CLASSIFICATION AND IMAGE GENERATION

A method which includes: Obtaining a training set which comprises: multiple data pairs each comprising: (i) a raw sensory signal acquired by a medical imaging system, and (ii) a processed image generated by the medical imaging system from the raw sensory signal; and a classification label for each of the data pairs. Based on the training set, training an artificial neural network (ANN), wherein the training comprises minimizing a global loss which is a weighted sum of: a loss between the classification labels and classification predictions by the ANN, and a similarity loss between the processed images and images generated by an intermediate layer of the ANN. The training is such that the trained ANN is configured, for a new raw sensory signal: to predict a new classification, and to generate a new image by the intermediate layer of the ANN.