74
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Convolutional neural networks: an overview and application in radiology

      review-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Abstract

          Convolutional neural network (CNN), a class of artificial neural networks that has become dominant in various computer vision tasks, is attracting interest across a variety of domains, including radiology. CNN is designed to automatically and adaptively learn spatial hierarchies of features through backpropagation by using multiple building blocks, such as convolution layers, pooling layers, and fully connected layers. This review article offers a perspective on the basic concepts of CNN and its application to various radiological tasks, and discusses its challenges and future directions in the field of radiology. Two challenges in applying CNN to radiological tasks, small dataset and overfitting, will also be covered in this article, as well as techniques to minimize them. Being familiar with the concepts and advantages, as well as limitations, of CNN is essential to leverage its potential in diagnostic radiology, with the goal of augmenting the performance of radiologists and improving patient care.

          Key Points

          • Convolutional neural network is a class of deep learning methods which has become dominant in various computer vision tasks and is attracting interest across a variety of domains, including radiology.

          • Convolutional neural network is composed of multiple building blocks, such as convolution layers, pooling layers, and fully connected layers, and is designed to automatically and adaptively learn spatial hierarchies of features through a backpropagation algorithm.

          • Familiarity with the concepts and advantages, as well as limitations, of convolutional neural network is essential to leverage its potential to improve radiologist performance and, eventually, patient care.

          Related collections

          Most cited references15

          • Record: found
          • Abstract: found
          • Article: not found

          The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository.

          The National Institutes of Health have placed significant emphasis on sharing of research data to support secondary research. Investigators have been encouraged to publish their clinical and imaging data as part of fulfilling their grant obligations. Realizing it was not sufficient to merely ask investigators to publish their collection of imaging and clinical data, the National Cancer Institute (NCI) created the open source National Biomedical Image Archive software package as a mechanism for centralized hosting of cancer related imaging. NCI has contracted with Washington University in Saint Louis to create The Cancer Imaging Archive (TCIA)-an open-source, open-access information resource to support research, development, and educational initiatives utilizing advanced medical imaging of cancer. In its first year of operation, TCIA accumulated 23 collections (3.3 million images). Operating and maintaining a high-availability image archive is a complex challenge involving varied archive-specific resources and driven by the needs of both image submitters and image consumers. Quality archives of any type (traditional library, PubMed, refereed journals) require management and customer service. This paper describes the management tasks and user support model for TCIA.
            Bookmark
            • Record: found
            • Abstract: not found
            • Conference Proceedings: not found

            Learning Deep Features for Discriminative Localization

              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Deep Convolutional Neural Network for Inverse Problems in Imaging.

              In this paper, we propose a novel deep convolutional neural network (CNN)-based algorithm for solving ill-posed inverse problems. Regularized iterative algorithms have emerged as the standard approach to ill-posed inverse problems in the past few decades. These methods produce excellent results, but can be challenging to deploy in practice due to factors including the high computational cost of the forward and adjoint operators and the difficulty of hyper parameter selection. The starting point of our work is the observation that unrolled iterative methods have the form of a CNN (filtering followed by point-wise nonlinearity) when the normal operator ( H*H where H* is the adjoint of the forward imaging operator, H ) of the forward model is a convolution. Based on this observation, we propose using direct inversion followed by a CNN to solve normal-convolutional inverse problems. The direct inversion encapsulates the physical model of the system, but leads to artifacts when the problem is ill-posed; the CNN combines multiresolution decomposition and residual learning in order to learn to remove these artifacts while preserving image structure. We demonstrate the performance of the proposed network in sparse-view reconstruction (down to 50 views) on parallel beam X-ray computed tomography in synthetic phantoms as well as in real experimental sinograms. The proposed network outperforms total variation-regularized iterative reconstruction for the more realistic phantoms and requires less than a second to reconstruct a 512 x 512 image on the GPU.
                Bookmark

                Author and article information

                Contributors
                +81-75-751-3760 , rickdom2610@gmail.com
                Journal
                Insights Imaging
                Insights Imaging
                Insights into Imaging
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                1869-4101
                22 June 2018
                22 June 2018
                August 2018
                : 9
                : 4
                : 611-629
                Affiliations
                [1 ]ISNI 0000 0004 0372 2033, GRID grid.258799.8, Department of Diagnostic Imaging and Nuclear Medicine, , Kyoto University Graduate School of Medicine, ; 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507 Japan
                [2 ]ISNI 0000 0001 2171 9952, GRID grid.51462.34, Department of Radiology, , Memorial Sloan Kettering Cancer Center, ; 1275 York Avenue, New York, NY 10065 USA
                [3 ]ISNI 0000 0004 0531 2775, GRID grid.411217.0, Preemptive Medicine and Lifestyle Disease Research Center, , Kyoto University Hospital, ; 53 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507 Japan
                Article
                639
                10.1007/s13244-018-0639-9
                6108980
                29934920
                02995aa6-0b2f-4d15-bf8c-f99aee2dd3dd
                © The Author(s) 2018

                Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 3 March 2018
                : 24 April 2018
                : 28 May 2018
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001691, Japan Society for the Promotion of Science;
                Award ID: JP16K19883
                Award Recipient :
                Categories
                Review
                Custom metadata
                © The Author(s) 2018

                Radiology & Imaging
                machine learning,deep learning,convolutional neural network,medical imaging,radiology

                Comments

                Comment on this article