Cargando…
Deep learning models for COVID-19 chest x-ray classification: Preventing shortcut learning using feature disentanglement
In response to the COVID-19 global pandemic, recent research has proposed creating deep learning based models that use chest radiographs (CXRs) in a variety of clinical tasks to help manage the crisis. However, the size of existing datasets of CXRs from COVID-19+ patients are relatively small, and r...
Autores principales: | Robinson, Caleb, Trivedi, Anusua, Blazes, Marian, Ortiz, Anthony, Desbiens, Jocelyn, Gupta, Sunil, Dodhia, Rahul, Bhatraju, Pavan K., Liles, W. Conrad, Lee, Aaron, Kalpathy-Cramer, Jayashree, Ferres, Juan M. Lavista |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7885941/ https://www.ncbi.nlm.nih.gov/pubmed/33594382 http://dx.doi.org/10.1101/2021.02.11.20196766 |
Ejemplares similares
-
Deep learning models for COVID-19 chest x-ray classification: Preventing shortcut learning using feature disentanglement
por: Trivedi, Anusua, et al.
Publicado: (2022) -
Effective deep learning approaches for predicting COVID-19 outcomes from chest computed tomography volumes
por: Ortiz, Anthony, et al.
Publicado: (2022) -
Machine learning-based derivation and external validation of a tool to predict death and development of organ failure in hospitalized patients with COVID-19
por: Xu, Yixi, et al.
Publicado: (2022) -
Machine Learning-based Derivation and External Validation of a Tool to Predict Death and Development of Organ Failure in Hospitalized Patients with COVID-19
por: Xu, Yixi, et al.
Publicado: (2021) -
Detecting shortcut learning for fair medical AI using shortcut testing
por: Brown, Alexander, et al.
Publicado: (2023)