Causality matters in medical imaging.
Nat Commun
; 11(1): 3673, 2020 07 22.
Article
en En
| MEDLINE
| ID: mdl-32699250
ABSTRACT
Causal reasoning can shed new light on the major challenges in machine learning for medical imaging scarcity of high-quality annotated data and mismatch between the development dataset and the target environment. A causal perspective on these issues allows decisions about data collection, annotation, preprocessing, and learning strategies to be made and scrutinized more transparently, while providing a detailed categorisation of potential biases and mitigation techniques. Along with worked clinical examples, we highlight the importance of establishing the causal relationship between images and their annotations, and offer step-by-step recommendations for future studies.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Diagnóstico por Imagen
/
Interpretación de Imagen Asistida por Computador
/
Aprendizaje Automático
Tipo de estudio:
Diagnostic_studies
/
Etiology_studies
/
Guideline
/
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Nat Commun
Asunto de la revista:
BIOLOGIA
/
CIENCIA
Año:
2020
Tipo del documento:
Article
País de afiliación:
Reino Unido