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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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| Yayımlandı: | Nat Commun |
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| Asıl Yazarlar: | , , |
| Materyal Türü: | Artigo |
| Dil: | Inglês |
| Baskı/Yayın Bilgisi: |
Nature Publishing Group UK
2020
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| Konular: | |
| Online Erişim: | 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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