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      Convolutional Neural Network Technology in Endoscopic Imaging: Artificial Intelligence for Endoscopy

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          Abstract

          Recently, significant improvements have been made in artificial intelligence. The artificial neural network was introduced in the 1950s. However, because of the low computing power and insufficient datasets available at that time, artificial neural networks suffered from overfitting and vanishing gradient problems for training deep networks. This concept has become more promising owing to the enhanced big data processing capability, improvement in computing power with parallel processing units, and new algorithms for deep neural networks, which are becoming increasingly successful and attracting interest in many domains, including computer vision, speech recognition, and natural language processing. Recent studies in this technology augur well for medical and healthcare applications, especially in endoscopic imaging. This paper provides perspectives on the history, development, applications, and challenges of deep-learning technology.

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          Most cited references57

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          Deep Residual Learning for Image Recognition

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            ImageNet: A large-scale hierarchical image database

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              Receptive fields and functional architecture of monkey striate cortex.

              1. The striate cortex was studied in lightly anaesthetized macaque and spider monkeys by recording extracellularly from single units and stimulating the retinas with spots or patterns of light. Most cells can be categorized as simple, complex, or hypercomplex, with response properties very similar to those previously described in the cat. On the average, however, receptive fields are smaller, and there is a greater sensitivity to changes in stimulus orientation. A small proportion of the cells are colour coded.2. Evidence is presented for at least two independent systems of columns extending vertically from surface to white matter. Columns of the first type contain cells with common receptive-field orientations. They are similar to the orientation columns described in the cat, but are probably smaller in cross-sectional area. In the second system cells are aggregated into columns according to eye preference. The ocular dominance columns are larger than the orientation columns, and the two sets of boundaries seem to be independent.3. There is a tendency for cells to be grouped according to symmetry of responses to movement; in some regions the cells respond equally well to the two opposite directions of movement of a line, but other regions contain a mixture of cells favouring one direction and cells favouring the other.4. A horizontal organization corresponding to the cortical layering can also be discerned. The upper layers (II and the upper two-thirds of III) contain complex and hypercomplex cells, but simple cells are virtually absent. The cells are mostly binocularly driven. Simple cells are found deep in layer III, and in IV A and IV B. In layer IV B they form a large proportion of the population, whereas complex cells are rare. In layers IV A and IV B one finds units lacking orientation specificity; it is not clear whether these are cell bodies or axons of geniculate cells. In layer IV most cells are driven by one eye only; this layer consists of a mosaic with cells of some regions responding to one eye only, those of other regions responding to the other eye. Layers V and VI contain mostly complex and hypercomplex cells, binocularly driven.5. The cortex is seen as a system organized vertically and horizontally in entirely different ways. In the vertical system (in which cells lying along a vertical line in the cortex have common features) stimulus dimensions such as retinal position, line orientation, ocular dominance, and perhaps directionality of movement, are mapped in sets of superimposed but independent mosaics. The horizontal system segregates cells in layers by hierarchical orders, the lowest orders (simple cells monocularly driven) located in and near layer IV, the higher orders in the upper and lower layers.
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                Author and article information

                Journal
                Clin Endosc
                Clin Endosc
                CE
                Clinical Endoscopy
                Korean Society of Gastrointestinal Endoscopy
                2234-2400
                2234-2443
                March 2020
                30 March 2020
                : 53
                : 2
                : 117-126
                Affiliations
                [1 ]Department of Convergence Medicine, University of Ulsan College of Medicine, Seoul, Korea
                [2 ]Promedius, Inc., Seoul, Korea
                [3 ]Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
                [4 ]Department of Radiology, Asan Medical Center, Seoul, Korea
                Author notes
                Correspondence: Namkug Kim Department of Radiology, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea Tel: +82-2-3010-6573, Fax: +82-2-2045-3426, E-mail: namkugkim@ 123456gmail.com
                Author information
                http://orcid.org/0000-0003-3420-7125
                http://orcid.org/0000-0002-5028-5716
                http://orcid.org/0000-0003-3255-1774
                http://orcid.org/0000-0001-5134-5517
                http://orcid.org/0000-0002-4250-4683
                http://orcid.org/0000-0002-9793-6379
                http://orcid.org/0000-0002-3438-2217
                Article
                ce-2020-054
                10.5946/ce.2020.054
                7137563
                32252504
                5edfe34d-a8c9-4a8d-8605-0756ae4e412d
                Copyright © 2020 Korean Society of Gastrointestinal Endoscopy

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 21 February 2020
                : 10 March 2020
                : 13 March 2020
                Categories
                Focused Review Series: Application of Artificial Intelligence in GI Endoscopy

                Radiology & Imaging
                artificial intelligence,convolutional neural network,deep learning,endoscopic imaging,machine learning

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