Weighted–VAE: A deep learning approach for multimodal data generation applied to experimental T. cruzi infection

Abstract

Chagas disease (CD), caused by the protozoan parasite Trypanosoma cruzi (T. cruzi), represents a major public health concern in most of the American continent and causes 12,000 deaths every year. CD clinically manifests in two phases (acute and chronic), and the diagnosis can result in complications due to the difference between phases and the long period between them. Still, strategies are lacking for the automatic diagnosis of healthy and T. cruzi-infected individuals with missing and limited data. In this work, we propose a Weighted Variational Auto-Encoder (W–VAE) for imputing and augmenting multimodal data to classify healthy individuals and individuals in the acute or chronic phases of T. cruzi infection from a murine model. W–VAE is a deep generative architecture trained with a new proposed loss function to which we added a weighting factor and a masking mechanism to improve the quality of the data generated. We imputed and augmented data using four modalities: electrocardiography signals, echocardiography images, Doppler spectrum, and ELISA antibody titers. We evaluated the generated data through different multi-classification tasks to identify healthy individuals and individuals in the acute or chronic phase of infection. In each multi-classification task, we assessed several classifiers, missing rates, and feature-selection methods. The best obtained accuracy was 92 ± 4% in training and 95% in the final test using a Gaussian Process Classifier with a missing rate of 50%. The accuracy achieved was 95% for individuals in healthy and acute phase and 100% for individuals in the chronic phase. Our approach can be useful in generating data to study the phases of T. cruzi infection.