Overall architecture of the W–VAE proposed for imputing and augmenting data. W–VAE is conditioned by the labels from the murine model (healthy individual, acute-phase individual, and chronic-phase individual).

Overall architecture of the W–VAE proposed for imputing and augmenting data. W–VAE is conditioned by the labels from the murine model (healthy individual, acute-phase individual, and chronic-phase individual).

<p>Overall architecture of the W–VAE proposed for imputing and augmenting data. W–VAE is conditioned by the labels from the murine model (healthy individual, acute-phase individual, and chronic-phase individual).</p>

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Bibliographic Details
Main Author:	Blanca Vazquez (2525026) (author)
Other Authors:	Nidiyare Hevia-Montiel (582955) (author), Jorge Perez-Gonzalez (20928228) (author), Paulina Haro (20928231) (author)
Published:	2025
Subjects:	Medicine Microbiology Immunology Biological Sciences not elsewhere classified Information Systems not elsewhere classified weighted – vae weighted variational auto w – vae gaussian process classifier final test using elisa antibody titers div >< p cd clinically manifests assessed several classifiers 92 ± 4 cd ), caused best obtained accuracy >- infected individuals augmenting multimodal data deep learning approach identify healthy individuals classify healthy individuals chronic ), accuracy achieved limited data generating data generated data data generated weighting factor trypanosoma cruzi selection methods protozoan parasite murine model masking mechanism long period electrocardiography signals echocardiography images doppler spectrum cruzi </ cruzi )</ complications due classification tasks classification task chronic phase chagas disease causes 12 american continent >, represents 50 %.
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