A mixed-scale dense convolutional neural network for image analysis.
Proc Natl Acad Sci U S A
; 115(2): 254-259, 2018 01 09.
Article
de En
| MEDLINE
| ID: mdl-29279403
ABSTRACT
Deep convolutional neural networks have been successfully applied to many image-processing problems in recent works. Popular network architectures often add additional operations and connections to the standard architecture to enable training deeper networks. To achieve accurate results in practice, a large number of trainable parameters are often required. Here, we introduce a network architecture based on using dilated convolutions to capture features at different image scales and densely connecting all feature maps with each other. The resulting architecture is able to achieve accurate results with relatively few parameters and consists of a single set of operations, making it easier to implement, train, and apply in practice, and automatically adapts to different problems. We compare results of the proposed network architecture with popular existing architectures for several segmentation problems, showing that the proposed architecture is able to achieve accurate results with fewer parameters, with a reduced risk of overfitting the training data.
Mots clés
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Imagerie diagnostique
/
/
Apprentissage machine
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Modèles théoriques
Type d'étude:
Diagnostic_studies
Langue:
En
Journal:
Proc Natl Acad Sci U S A
Année:
2018
Type de document:
Article