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Training confounder-free deep learning models for medical applications
The presence of confounding effects (or biases) is one of the most critical challenges in using deep learning to advance discovery in medical imaging studies. Confounders affect the relationship between input data (e.g., brain MRIs) and output variables (e.g., diagnosis). Improper modeling of those...
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| Pubblicato in: | Nat Commun |
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| Autori principali: | , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
Nature Publishing Group UK
2020
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7691500/ https://ncbi.nlm.nih.gov/pubmed/33243992 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1038/s41467-020-19784-9 |
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