Unsupervised white blood cell characterization in the latent space of a variational autoencoder

Abstract

Leukemia diagnosis and therapy planning are both based on classifying peripheral blood images, under a high inter/intra observer variability scenario. In such applications, automatic image processing and classification strategies have obtained outstanding recognition results, however they are fully dependent on the quality of the annotated data. Unlike supervised classification approaches which built upon label-transformations, the herein presented methodology introduces an unsupervised White Blood Cell characterization in the latent space of a Variational Autoencoder (VAE). The latent space is constructed upon 128 parameters from 64 gaussian distributions and then a k-means clustering may retrieve leukemia diagnostic meaningful cell groups. The whole procedure is twofold assessed: 1) evaluation of the 128 dimension VAE latent space for differentiating cells with higher diagnostic value (blast cells) from other peripheral blood components under multiple supervised classification strategies, and 2) quantification of VAE-parameter clustering capacity to unsupervised separation of blast and non-blast cells. Obtained accuracies of each experiment, 0.888 and 0.757 respectively, suggest that the presented strategy successfully characterizes white blood cells and provides a representation space where subtle cell differences can be objectively measured.