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Tiled sparse coding in eigenspaces for the COVID-19 diagnosis in chest X-ray images
In this work, we propose a classification framework based on sparse coding in order to identify the pneumonia patterns associated with different pathologies.
Probabilistic combination of eigenlungs-based classifiers for COVID-19 diagnosis in chest CT images
The outbreak of the COVID-19 (Coronavirus disease 2019) pandemic has changed the world.
Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Data
Our system is capable of learning from a mixture of limited labeled and unlabeled data where supervised learners fail due to a lack of sufficient amount of labeled data.
Uncertainty-driven ensembles of deep architectures for multiclass classification. Application to COVID-19 diagnosis in chest X-ray images
Respiratory diseases kill million of people each year.
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