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Expert-level detection of pathologies from unannotated chest X-ray images via self-supervised learning

In tasks involving the interpretation of medical images, suitably trained machine-learning models often exceed the performance of medical experts. Yet such a high-level of performance typically requires that the models be trained with relevant datasets that have been painstakingly annotated by exper...

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Detalles Bibliográficos
Autores principales: Tiu, Ekin, Talius, Ellie, Patel, Pujan, Langlotz, Curtis P., Ng, Andrew Y., Rajpurkar, Pranav
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9792370/
https://www.ncbi.nlm.nih.gov/pubmed/36109605
http://dx.doi.org/10.1038/s41551-022-00936-9
Descripción
Sumario:In tasks involving the interpretation of medical images, suitably trained machine-learning models often exceed the performance of medical experts. Yet such a high-level of performance typically requires that the models be trained with relevant datasets that have been painstakingly annotated by experts. Here we show that a self-supervised model trained on chest X-ray images that lack explicit annotations performs pathology-classification tasks with accuracies comparable to those of radiologists. On an external validation dataset of chest X-rays, the self-supervised model outperformed a fully supervised model in the detection of three pathologies (out of eight), and the performance generalized to pathologies that were not explicitly annotated for model training, to multiple image-interpretation tasks and to datasets from multiple institutions.