Deep Neural Network With Consistency Regularization of Multi-Output Channels for Improved Tumor Detection and Delineation.
IEEE Trans Med Imaging
; 40(12): 3369-3378, 2021 12.
Article
en En
| MEDLINE
| ID: mdl-34048339
ABSTRACT
Deep learning is becoming an indispensable tool for imaging applications, such as image segmentation, classification, and detection. In this work, we reformulate a standard deep learning problem into a new neural network architecture with multi-output channels, which reflects different facets of the objective, and apply the deep neural network to improve the performance of image segmentation. By adding one or more interrelated auxiliary-output channels, we impose an effective consistency regularization for the main task of pixelated classification (i.e., image segmentation). Specifically, multi-output-channel consistency regularization is realized by residual learning via additive paths that connect main-output channel and auxiliary-output channels in the network. The method is evaluated on the detection and delineation of lung and liver tumors with public data. The results clearly show that multi-output-channel consistency implemented by residual learning improves the standard deep neural network. The proposed framework is quite broad and should find widespread applications in various deep learning problems.
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Redes Neurales de la Computación
/
Neoplasias
Tipo de estudio:
Diagnostic_studies
Límite:
Humans
Idioma:
En
Revista:
IEEE Trans Med Imaging
Año:
2021
Tipo del documento:
Article