Hybrid feature engineering of medical data via variational autoencoders with triplet loss: a covid-19 prognosis study

HIGHLIGHTS

• who: Mahdi Mahdavi from the DepartmentShahid Beheshti University of Isfahan, Isfahan, u201173441, Iran have published the research work: Hybrid feature engineering of medical data via variational autoencoders with triplet loss: a COVID-19 prognosis study, in the Journal: Scientific Reports Scientific Reports
• what: Autoencoders (AEs) are state-of-the-art unsupervised deep learning frameworks with the main purpose of learning informative representations, often in lowered dimensions, from the input data to reconstruct them. After observing the results from the previous section, the authors aimed to see how the latent representations from AE models will perform . . .
  

   

  

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