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      Convolutional Neural Networks for Spectroscopic Analysis in Retinal Oximetry

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          Abstract

          Retinal oximetry is a non-invasive technique to investigate the hemodynamics, vasculature and health of the eye. Current techniques for retinal oximetry have been plagued by quantitatively inconsistent measurements and this has greatly limited their adoption in clinical environments. To become clinically relevant oximetry measurements must become reliable and reproducible across studies and locations. To this end, we have developed a convolutional neural network algorithm for multi-wavelength oximetry, showing a greatly improved calculation performance in comparison to previously reported techniques. The algorithm is calibration free, performs sensing of the four main hemoglobin conformations with no prior knowledge of their characteristic absorption spectra and, due to the convolution-based calculation, is invariable to spectral shifting. We show, herein, the dramatic performance improvements in using this algorithm to deduce effective oxygenation (SO 2), as well as the added functionality to accurately measure fractional oxygenation ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\bf{SO}}}_{{\bf{2}}}^{{\boldsymbol{f}}{\boldsymbol{r}}}$$\end{document} ). Furthermore, this report compares, for the first time, the relative performance of several previously reported multi-wavelength oximetry algorithms in the face of controlled spectral variations. The improved ability of the algorithm to accurately and independently measure hemoglobin concentrations offers a high potential tool for disease diagnosis and monitoring when applied to retinal spectroscopy.

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          Most cited references 70

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          Face recognition: a convolutional neural-network approach.

          We present a hybrid neural-network for human face recognition which compares favourably with other methods. The system combines local image sampling, a self-organizing map (SOM) neural network, and a convolutional neural network. The SOM provides a quantization of the image samples into a topological space where inputs that are nearby in the original space are also nearby in the output space, thereby providing dimensionality reduction and invariance to minor changes in the image sample, and the convolutional neural network provides partial invariance to translation, rotation, scale, and deformation. The convolutional network extracts successively larger features in a hierarchical set of layers. We present results using the Karhunen-Loeve transform in place of the SOM, and a multilayer perceptron (MLP) in place of the convolutional network for comparison. We use a database of 400 images of 40 individuals which contains quite a high degree of variability in expression, pose, and facial details. We analyze the computational complexity and discuss how new classes could be added to the trained recognizer.
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            Automatic Segmentation of MR Brain Images With a Convolutional Neural Network

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              Diabetic Retinopathy: Vascular and Inflammatory Disease

              Diabetic retinopathy (DR) is the leading cause of visual impairment in the working-age population of the Western world. The pathogenesis of DR is complex and several vascular, inflammatory, and neuronal mechanisms are involved. Inflammation mediates structural and molecular alterations associated with DR. However, the molecular mechanisms underlying the inflammatory pathways associated with DR are not completely characterized. Previous studies indicate that tissue hypoxia and dysregulation of immune responses associated with diabetes mellitus can induce increased expression of numerous vitreous mediators responsible for DR development. Thus, analysis of vitreous humor obtained from diabetic patients has made it possible to identify some of the mediators (cytokines, chemokines, and other factors) responsible for DR pathogenesis. Further studies are needed to better understand the relationship between inflammation and DR. Herein the main vitreous-related factors triggering the occurrence of retinal complication in diabetes are highlighted.
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                Author and article information

                Contributors
                dccote@cervo.ulaval.ca
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                6 August 2019
                6 August 2019
                2019
                : 9
                Affiliations
                [1 ]ISNI 0000 0004 1936 8390, GRID grid.23856.3a, Université Laval, CERVO Brain Research Center, Neuroscience, Quebec City, ; Québec, G1V 0A6 Canada
                [2 ]ISNI 0000 0004 1936 8390, GRID grid.23856.3a, Université Laval, Center for optics, photonics and lasers (COPL), Physics Engineering, Quebec City, ; Québec, G1V 0A6 Canada
                [3 ]ISNI 0000 0004 1936 8390, GRID grid.23856.3a, Université Laval, Department of software engineering and informatics, Quebec city, ; Québec, G1V 0A6 Canada
                [4 ]Zilia inc., Quebec City, Québec, G1V 0A6 Canada
                [5 ]GRID grid.17089.37, University of Alberta, Chemical and Materials Engineering, ; Edmonton, Alberta T6G 1H9 Canada
                Article
                47621
                10.1038/s41598-019-47621-7
                6684811
                31388136
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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