Bias and Class Imbalance in Oncologic Data—Towards Inclusive and Transferrable AI in Large Scale Oncology Data Sets.
Simple Summary: Large-scale medical data carries significant areas of underrepresentation and bias at all levels: clinical, biological, and management. Resulting data sets and outcome measures reflect these shortcomings in clinical, imaging, and omics data with class imbalance emerging as the single...
| Publicado en: | Cancers Vol. 14; no. 12; pp. 2897 - 2913 |
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| Autores principales: | , , , |
| Formato: | equations & formulas review tables/charts Journal Article |
| Publicado: |
MDPI
Jun2022
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| Acceso en línea: | Ver este registro en EBSCOhost |