Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Fairness in Cardiac Magnetic Resonance Imaging: Assessing Sex and Racial Bias in Deep Learning-Based Segmentation
66
Zitationen
9
Autoren
2022
Jahr
Abstract
We have shown that racial bias can exist in DL-based cine CMR segmentation models when training with a database that is sex-balanced but not race-balanced such as the UK Biobank.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.231 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.084 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.444 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.423 Zit.
Autoren
Institutionen
- King's College London(GB)
- University Medical Center Utrecht(NL)
- Guy's and St Thomas' NHS Foundation Trust(GB)
- University of Oxford(GB)
- St Bartholomew's Hospital(GB)
- National Institute for Health Research(GB)
- Health Data Research UK(GB)
- Barts Health NHS Trust(GB)
- The Alan Turing Institute(GB)
- William Harvey Research Institute(GB)
- British Heart Foundation(GB)