Image Class Translation Distance: A Novel Interpretable Feature for Image Classification

We propose a novel application of image translation networks for image classification and demonstrate its potential as a more interpretable alternative to conventional black box classification networks. We train a network to translate images between possible classes, and then quantify translation distance, i.e. the degree of alteration needed to conform an image to one class or another. These translation distances can then be examined for clusters and trends, and can be fed directly to a simple classifier (e.g. a support vector machine, SVM), providing comparable accuracy compared to a conventional end-to-end convolutional neural network classifier. In addition, visual inspection of translated images can reveal class-specific characteristics and biases in the training sets, such as visual artifacts that are more frequently observed in one class or another. We demonstrate the approach on a toy 2-class scenario, apples versus oranges, and then apply it to two medical imaging tasks: detecting melanoma from photographs of pigmented lesions and classifying 6 cell types in a bone marrow biopsy smear. This novel application of image-to-image networks shows the potential of the technology to go beyond imagining different stylistic changes and to provide greater insight into image classification and medical imaging datasets.