Systems and methods for interpretable classification of images using inherently explainable neural networks

Abstract

An artificial intelligence-based image processing system comprises a processor that executes instructions stored on a memory to classify an input image with a prototypical part neural network including a backbone subnetwork, a prototype subnetwork, and a readout subnetwork to produce an interpretable classification of the input image including one or a combination of a classification result of the input image and an interpretation of the classification result. The backbone subnetwork is trained with machine learning to process the input image with an incomplete sequence of active convolutional layers producing feature embeddings representing features extracted from pixels of different regions of the input image. The prototype subnetwork is trained to compare the feature embeddings with prototypical feature embeddings to produce results of comparison and the readout subnetwork is configured to analyze the results of comparison to produce the interpretable classification of the input image.