Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 2 de 2
Filtrar
1.
Journal of Biomedical Engineering ; (6): 493-498, 2019.
Artículo en Chino | WPRIM | ID: wpr-774180

RESUMEN

The human brain deteriorates as we age, and the rate and the trajectories of these changes significantly vary among brain regions and among individuals. Because neuroimaging data are potentially important indicators of individual's brain health, they are commonly used in brain age prediction. In this review, we summarize brain age prediction model from neuroimaging-based studies in the last ten years. The studies are categorized based on their image modalities and feature types. The results indicate that the prediction frameworks based on neuroimaging holds promise toward individualized brain age prediction. Finally, we addressed the challenges in brain age prediction and suggested some future research directions.


Asunto(s)
Humanos , Envejecimiento , Encéfalo , Diagnóstico por Imagen , Fisiología , Neuroimagen
2.
Journal of Biomedical Engineering ; (6): 670-676, 2019.
Artículo en Chino | WPRIM | ID: wpr-774156

RESUMEN

Computer-aided diagnosis based on computed tomography (CT) image can realize the detection and classification of pulmonary nodules, and improve the survival rate of early lung cancer, which has important clinical significance. In recent years, with the rapid development of medical big data and artificial intelligence technology, the auxiliary diagnosis of lung cancer based on deep learning has gradually become one of the most active research directions in this field. In order to promote the deep learning in the detection and classification of pulmonary nodules, we reviewed the research progress in this field based on the relevant literatures published at domestic and overseas in recent years. This paper begins with a brief introduction of two widely used lung CT image databases: lung image database consortium and image database resource initiative (LIDC-IDRI) and Data Science Bowl 2017. Then, the detection and classification of pulmonary nodules based on different network structures are introduced in detail. Finally, some problems of deep learning in lung CT image nodule detection and classification are discussed and conclusions are given. The development prospect is also forecasted, which provides reference for future application research in this field.


Asunto(s)
Humanos , Aprendizaje Profundo , Neoplasias Pulmonares , Diagnóstico por Imagen , Interpretación de Imagen Radiográfica Asistida por Computador , Reproducibilidad de los Resultados , Nódulo Pulmonar Solitario , Diagnóstico por Imagen , Tomografía Computarizada por Rayos X
SELECCIÓN DE REFERENCIAS
DETALLE DE LA BÚSQUEDA