CAB U-Net: An end-to-end category attention boosting algorithm for segmentation.
Comput Med Imaging Graph
; 84: 101764, 2020 09.
Article
en En
| MEDLINE
| ID: mdl-32721853
With the development of machine learning and artificial intelligence, many convolutional neural networks (CNNs) based segmentation methods have been proposed for 3D cardiac segmentation. In this paper, we propose the category attention boosting (CAB) module, which combines the deep network calculation graph with the boosting method. On the one hand, we add the attention mechanism into the gradient boosting process, which enhances the information of coarse segmentation without high computation cost. On the other hand, we introduce the CAB module into the 3D U-Net segmentation network and construct a new multi-scale boosting model CAB U-Net which strengthens the gradient flow in the network and makes full use of the low resolution feature information. Thanks to the advantage that end-to-end networks can adaptively adjust the internal parameters, CAB U-Net can make full use of the complementary effects among different base learners. Extensive experiments on public datasets show that our approach can achieve superior performance over the state-of-the-art methods.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Inteligencia Artificial
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
Comput Med Imaging Graph
Asunto de la revista:
DIAGNOSTICO POR IMAGEM
Año:
2020
Tipo del documento:
Article
País de afiliación:
China
Pais de publicación:
Estados Unidos