Automated acquisition of explainable knowledge from unannotated histopathology images.
Nat Commun
; 10(1): 5642, 2019 12 18.
Article
en En
| MEDLINE
| ID: mdl-31852890
ABSTRACT
Deep learning algorithms have been successfully used in medical image classification. In the next stage, the technology of acquiring explainable knowledge from medical images is highly desired. Here we show that deep learning algorithm enables automated acquisition of explainable features from diagnostic annotation-free histopathology images. We compare the prediction accuracy of prostate cancer recurrence using our algorithm-generated features with that of diagnosis by expert pathologists using established criteria on 13,188 whole-mount pathology images consisting of over 86 billion image patches. Our method not only reveals findings established by humans but also features that have not been recognized, showing higher accuracy than human in prognostic prediction. Combining both our algorithm-generated features and human-established criteria predicts the recurrence more accurately than using either method alone. We confirm robustness of our method using external validation datasets including 2276 pathology images. This study opens up fields of machine learning analysis for discovering uncharted knowledge.
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Patología
/
Procesamiento de Imagen Asistido por Computador
/
Conocimiento
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Nat Commun
Asunto de la revista:
BIOLOGIA
/
CIENCIA
Año:
2019
Tipo del documento:
Article
País de afiliación:
Japón