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...

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Detalles Bibliográficos
Autores principales: Tasci, Erdal, Zhuge, Ying, Camphausen, Kevin, Krauze, Andra V.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Review
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9221277/
https://www.ncbi.nlm.nih.gov/pubmed/35740563
http://dx.doi.org/10.3390/cancers14122897

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9221277/
https://www.ncbi.nlm.nih.gov/pubmed/35740563
http://dx.doi.org/10.3390/cancers14122897

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