A mixed-scale dense convolutional neural network for image analysis

Pelt, Daniël M.; Sethian, James A.

doi:10.1073/pnas.1715832114

Title: 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:

Pelt, Daniël M. ^[1]; Sethian, James A. ^[2]

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:: Tue Dec 26 00:00:00 EST 2017

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.

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                    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

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                    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.

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@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         = {Tue Dec 26 00:00:00 EST 2017},

  month        = {Tue Dec 26 00:00:00 EST 2017}

}

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Cited by: 146 works

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Figures / Tables:

Fig. 1: A schematic representation of a two-layer CNN with input x, output y, and feature maps z₁ and z₂. Arrows represent convolutions with nonlinear activation.

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Figures / Tables found in this record:

Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.

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Title: A mixed-scale dense convolutional neural network for image analysis

Abstract

Citation Formats

Figures / Tables:

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