Deep Metric Learning for Transparent Classification of Covid-19 X-Ray Images

This work proposes an interpretable classifier for automatic Covid-19 classification using chest X-ray images. It is based on a deep learning model, in particular, a triplet network, devoted to finding an effective image embedding. Such embedding is a non-linear projection of the images into a space of reduced dimension, where homogeneity and separation of the classes measured by a predefined metric are improved. A KNearest Neighbor classifier is the interpretable model used for the final classification. Results on public datasets show that the proposed methodology can reach comparable results with state of the art in terms of accuracy, with the advantage of providing interpretability to the classification, a characteristic which can be very useful in the medical domain, e.g. in a decision support system.