A mixed-scale dense convolutional neural network for image analysis
Abstract
We report that 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. Lastly, 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.
- Authors:
-
- Center for Applied Mathematics for Energy Research Applications, Lawrence Berkeley National Laboratory, Berkeley, CA 94720,
- Center for Applied Mathematics for Energy Research Applications, Lawrence Berkeley National Laboratory, Berkeley, CA 94720,, Department of Mathematics, University of California, Berkeley, CA 94720
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Office of Science (SC), Biological and Environmental Research (BER)
- OSTI Identifier:
- 1414877
- Alternate Identifier(s):
- OSTI ID: 1485062
- Grant/Contract Number:
- AC03-76SF00098; AC02-05CH11231
- Resource Type:
- Published Article
- Journal Name:
- Proceedings of the National Academy of Sciences of the United States of America
- Additional Journal Information:
- Journal Name: Proceedings of the National Academy of Sciences of the United States of America Journal Volume: 115 Journal Issue: 2; Journal ID: ISSN 0027-8424
- Publisher:
- Proceedings of the National Academy of Sciences
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; image segmentation; machine learning; convolution neural networks
Citation Formats
Pelt, Daniël M., and Sethian, James A.. A mixed-scale dense convolutional neural network for image analysis. United States: N. p., 2017.
Web. doi:10.1073/pnas.1715832114.
Pelt, Daniël M., & Sethian, James A.. A mixed-scale dense convolutional neural network for image analysis. United States. https://doi.org/10.1073/pnas.1715832114
Pelt, Daniël M., and Sethian, James A.. Tue .
"A mixed-scale dense convolutional neural network for image analysis". United States. https://doi.org/10.1073/pnas.1715832114.
@article{osti_1414877,
title = {A mixed-scale dense convolutional neural network for image analysis},
author = {Pelt, Daniël M. and Sethian, James A.},
abstractNote = {We report that 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. Lastly, 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.},
doi = {10.1073/pnas.1715832114},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
number = 2,
volume = 115,
place = {United States},
year = {2017},
month = {12}
}
https://doi.org/10.1073/pnas.1715832114
Web of Science
Figures / Tables:

Works referenced in this record:
Superparsing: Scalable Nonparametric Image Parsing with Superpixels
journal, October 2012
- Tighe, Joseph; Lazebnik, Svetlana
- International Journal of Computer Vision, Vol. 101, Issue 2
PyCUDA and PyOpenCL: A scripting-based approach to GPU run-time code generation
journal, March 2012
- Klöckner, Andreas; Pinto, Nicolas; Lee, Yunsup
- Parallel Computing, Vol. 38, Issue 3
ImageNet: A large-scale hierarchical image database
conference, June 2009
- Deng, Jia; Dong, Wei; Socher, Richard
- 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops), 2009 IEEE Conference on Computer Vision and Pattern Recognition
Deep learning
journal, May 2015
- LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey
- Nature, Vol. 521, Issue 7553
Fully Convolutional Networks for Semantic Segmentation
journal, April 2017
- Shelhamer, Evan; Long, Jonathan; Darrell, Trevor
- IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 39, Issue 4
Semantic object classes in video: A high-definition ground truth database
journal, January 2009
- Brostow, Gabriel J.; Fauqueur, Julien; Cipolla, Roberto
- Pattern Recognition Letters, Vol. 30, Issue 2
Generalizing the Hough transform to detect arbitrary shapes
journal, January 1981
- Ballard, D. H.
- Pattern Recognition, Vol. 13, Issue 2
Radio frequency interference mitigation using deep convolutional neural networks
journal, January 2017
- Akeret, J.; Chang, C.; Lucchi, A.
- Astronomy and Computing, Vol. 18
Image-to-Image Translation with Conditional Adversarial Networks
conference, July 2017
- Isola, Phillip; Zhu, Jun-Yan; Zhou, Tinghui
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Caffe: Convolutional Architecture for Fast Feature Embedding
conference, January 2014
- Jia, Yangqing; Shelhamer, Evan; Donahue, Jeff
- Proceedings of the ACM International Conference on Multimedia - MM '14
Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique
journal, July 2001
- Yongbum Lee, ; Hara, T.; Fujita, H.
- IEEE Transactions on Medical Imaging, Vol. 20, Issue 7
Figures / Tables found in this record: