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<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">bsuir</journal-id><journal-title-group><journal-title xml:lang="ru">Доклады БГУИР</journal-title><trans-title-group xml:lang="en"><trans-title>Doklady BGUIR</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">1729-7648</issn><issn pub-type="epub">2708-0382</issn><publisher><publisher-name>БГУИР</publisher-name></publisher></journal-meta><article-meta><article-id custom-type="elpub" pub-id-type="custom">bsuir-1090</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>ЭЛЕКТРОНИКА, РАДИОФИЗИКА, РАДИОТЕХНИКА, ИНФОРМАТИКА</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>ELECTRONICS, RADIOPHYSICS, RADIOENGINEERING, INFORMATICS</subject></subj-group></article-categories><title-group><article-title>МОРФОЛОГИЧЕСКИЙ И СПЕКТРАЛЬНЫЙ АНАЛИЗ ГИСТОЛОГИЧЕСКОЙ ТКАНИ С ИСПОЛЬЗОВАНИЕМ ГЛУБОКИХ СВЕРТОЧНЫХ СЕТЕЙ</article-title><trans-title-group xml:lang="en"><trans-title>Morphological and spectral analysis of histological tissue with the use of deep convolutional networks</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Трухан</surname><given-names>С. В.</given-names></name><name name-style="western" xml:lang="en"><surname>Trukhan</surname><given-names>S. V.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Трухан Станислав Вячеславович - аспирант ОИПИ НАН Беларуси</p><p>220012, г. Минск, ул. Сурганова, 6</p><p>тел. +375-29-614-37-27</p></bio><bio xml:lang="en"><p>Trukhan Stanislau Vyacheslavovich - PG student of UIIP NAS of Belarus</p><p>220012, Republic of Belarus, Minsk, Surganova st., 6</p><p>tel.+375-29-614-37-27</p></bio><email xlink:type="simple">stas.truhan@gmail.com</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Недзьведь</surname><given-names>А. М.</given-names></name><name name-style="western" xml:lang="en"><surname>Nedzved</surname><given-names>A. M.</given-names></name></name-alternatives><bio xml:lang="ru"><p>доктор  технических  наук, профессор,  главный  научный  сотрудник  ОИПИ НАН Беларуси</p></bio><bio xml:lang="en"><p>D.Sci, professor, chief researcher of UIIP NAS of Belarus</p></bio><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Колер</surname><given-names>А.</given-names></name><name name-style="western" xml:lang="en"><surname>Kohler</surname><given-names>A.</given-names></name></name-alternatives><bio xml:lang="ru"><p>доктор  естественных  наук,  профессор физики  кафедры  RealTek</p><p>г. Ос</p></bio><bio xml:lang="en"><p>PhD, professor in physics at RealTek</p></bio><xref ref-type="aff" rid="aff-2"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>Объединенный институт проблем информатики НАН Беларуси</institution></aff><aff xml:lang="en"><institution>United institute of informatics problems of National academy of sciences of Belarus</institution></aff></aff-alternatives><aff-alternatives id="aff-2"><aff xml:lang="ru"><institution>Норвежский университет естественных наук</institution></aff><aff xml:lang="en"><institution>NMBU</institution></aff></aff-alternatives><pub-date pub-type="collection"><year>2019</year></pub-date><pub-date pub-type="epub"><day>04</day><month>06</month><year>2019</year></pub-date><volume>0</volume><issue>4</issue><fpage>25</fpage><lpage>31</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Трухан С.В., Недзьведь А.М., Колер А., 2019</copyright-statement><copyright-year>2019</copyright-year><copyright-holder xml:lang="ru">Трухан С.В., Недзьведь А.М., Колер А.</copyright-holder><copyright-holder xml:lang="en">Trukhan S.V., Nedzved A.M., Kohler A.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://doklady.bsuir.by/jour/article/view/1090">https://doklady.bsuir.by/jour/article/view/1090</self-uri><abstract><p>Представлены результаты исследования применения глубоких сверточных нейронных сетей к гиперспектральным изображениям.</p></abstract><trans-abstract xml:lang="en"><p>The results of a study of the application of deep neural convolutional networks to hyperspectral images are presented.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>гиперспектральное изображение</kwd><kwd>глубокие сверточные нейронные сети</kwd></kwd-group><kwd-group xml:lang="en"><kwd>hyperspectral image</kwd><kwd>deep convolution neural networks</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Marker-free automated histopathological annotation of lung tumour subtypes by FTIR imaging / F. Großerueschkamp [et al.] // Analyst. 2015. Vol. 140, № 7. P. 2114–2120.</mixed-citation><mixed-citation xml:lang="en">Marker-free automated histopathological annotation of lung tumour subtypes by FTIR imaging / F. Großerueschkamp [et al.] // Analyst. 2015. Vol. 140, № 7. P. 2114–2120.</mixed-citation></citation-alternatives></ref><ref id="cit2"><label>2</label><citation-alternatives><mixed-citation xml:lang="ru">Robust classification of low-grade cervical cytology following analysis with ATR-FTIR spectroscopy and subsequent application of self-learning classifier eClass / J.G. Kelly [et al.] // Anal. Bioanal. Chem. 2010. Vol. 398, № 5. P. 2191–2201.</mixed-citation><mixed-citation xml:lang="en">Robust classification of low-grade cervical cytology following analysis with ATR-FTIR spectroscopy and subsequent application of self-learning classifier eClass / J.G. Kelly [et al.] // Anal. Bioanal. Chem. 2010. Vol. 398, № 5. P. 2191–2201.</mixed-citation></citation-alternatives></ref><ref id="cit3"><label>3</label><citation-alternatives><mixed-citation xml:lang="ru">Distinction of cervical cancer biopsies by use of infrared microspectroscopy and probabilistic neural networks / A. Podshyvalov [et al.] // Appl. Opt. 2005. Vol. 44, № 18. P. 3725.</mixed-citation><mixed-citation xml:lang="en">Distinction of cervical cancer biopsies by use of infrared microspectroscopy and probabilistic neural networks / A. Podshyvalov [et al.] // Appl. Opt. 2005. Vol. 44, № 18. P. 3725.</mixed-citation></citation-alternatives></ref><ref id="cit4"><label>4</label><citation-alternatives><mixed-citation xml:lang="ru">Udelhoven T., Novozhilov M., Schmitt J. The NeuroDeveloper®: a tool for modular neural classification of spectroscopic data // Chemom. Intell. Lab. Syst. 2003. Vol. 66, № 2. P. 219–226.</mixed-citation><mixed-citation xml:lang="en">Udelhoven T., Novozhilov M., Schmitt J. The NeuroDeveloper®: a tool for modular neural classification of spectroscopic data // Chemom. Intell. Lab. Syst. 2003. Vol. 66, № 2. P. 219–226.</mixed-citation></citation-alternatives></ref><ref id="cit5"><label>5</label><citation-alternatives><mixed-citation xml:lang="ru">Evaluation and discrimination of simvastatin-induced structural alterations in proteins of different rat tissues by FTIR spectroscopy and neural network analysis / S. Garip [et al.] // Analyst. 2010. Vol. 135. P. 3233–3241.</mixed-citation><mixed-citation xml:lang="en">Evaluation and discrimination of simvastatin-induced structural alterations in proteins of different rat tissues by FTIR spectroscopy and neural network analysis / S. Garip [et al.] // Analyst. 2010. Vol. 135. P. 3233–3241.</mixed-citation></citation-alternatives></ref><ref id="cit6"><label>6</label><citation-alternatives><mixed-citation xml:lang="ru">Combining random forest and 2D correlation analysis to identify serum spectral signatures for neuro-oncology / B.R. Smith [et al.] // Analyst. 2016. Vol. 141, № 12. P. 3668–3678.</mixed-citation><mixed-citation xml:lang="en">Combining random forest and 2D correlation analysis to identify serum spectral signatures for neuro-oncology / B.R. Smith [et al.] // Analyst. 2016. Vol. 141, № 12. P. 3668–3678.</mixed-citation></citation-alternatives></ref><ref id="cit7"><label>7</label><citation-alternatives><mixed-citation xml:lang="ru">An investigation of the RWPE prostate derived family of cell lines using FTIR spectroscopy / M.J. Baker [et al.] // Analyst. 2010. Vol. 135, № 5. P. 887–894.</mixed-citation><mixed-citation xml:lang="en">An investigation of the RWPE prostate derived family of cell lines using FTIR spectroscopy / M.J. Baker [et al.] // Analyst. 2010. Vol. 135, № 5. P. 887–894.</mixed-citation></citation-alternatives></ref><ref id="cit8"><label>8</label><citation-alternatives><mixed-citation xml:lang="ru">Vibrational biospectroscopy coupled with multivariate analysis extracts potentially diagnostic features in blood plasma/serum of ovarian cancer patients / G.L. Owens [et al.] // J. Biophotonics. 2014. Vol. 7, № 3–4. P. 200–209.</mixed-citation><mixed-citation xml:lang="en">Vibrational biospectroscopy coupled with multivariate analysis extracts potentially diagnostic features in blood plasma/serum of ovarian cancer patients / G.L. Owens [et al.] // J. Biophotonics. 2014. Vol. 7, № 3–4. P. 200–209.</mixed-citation></citation-alternatives></ref><ref id="cit9"><label>9</label><citation-alternatives><mixed-citation xml:lang="ru">Fourier-transform infrared spectroscopy coupled with a classification machine for the analysis of blood plasma or serum: a novel diagnostic approach for ovarian cancer / K. Gajjar [et al.] // Analyst. 2013. Vol. 138, № 14. P. 3917.</mixed-citation><mixed-citation xml:lang="en">Fourier-transform infrared spectroscopy coupled with a classification machine for the analysis of blood plasma or serum: a novel diagnostic approach for ovarian cancer / K. Gajjar [et al.] // Analyst. 2013. Vol. 138, № 14. P. 3917.</mixed-citation></citation-alternatives></ref><ref id="cit10"><label>10</label><citation-alternatives><mixed-citation xml:lang="ru">Integrating spatial, morphological, and textural information for improved cell type differentiation using Raman microscopy / S.D. Krauß [et al.] // J. Chemom. 2018. Vol. 32, № 1.</mixed-citation><mixed-citation xml:lang="en">Integrating spatial, morphological, and textural information for improved cell type differentiation using Raman microscopy / S.D. Krauß [et al.] // J. Chemom. 2018. Vol. 32, № 1.</mixed-citation></citation-alternatives></ref><ref id="cit11"><label>11</label><citation-alternatives><mixed-citation xml:lang="ru">Krizhevsky A., Sutskever I. , Hinton G.E. ImageNet Classification with Deep Convolutional Neural Networks // NIPS. 2012. P. 1–9.</mixed-citation><mixed-citation xml:lang="en">Krizhevsky A., Sutskever I. , Hinton G.E. ImageNet Classification with Deep Convolutional Neural Networks // NIPS. 2012. P. 1–9.</mixed-citation></citation-alternatives></ref><ref id="cit12"><label>12</label><citation-alternatives><mixed-citation xml:lang="ru">Resonant Mie scattering in infrared spectroscopy of biological materials--understanding the «dispersion artefact» / P. Bassan [et al.] // Analyst. 2009. Vol. 134, № 8. P. 1586–1593.</mixed-citation><mixed-citation xml:lang="en">Resonant Mie scattering in infrared spectroscopy of biological materials--understanding the «dispersion artefact» / P. Bassan [et al.] // Analyst. 2009. Vol. 134, № 8. P. 1586–1593.</mixed-citation></citation-alternatives></ref><ref id="cit13"><label>13</label><citation-alternatives><mixed-citation xml:lang="ru">Breiman L. Random Forests // Mach. Learn. 1999. Vol. 45, № 5. P. 1–35.</mixed-citation><mixed-citation xml:lang="en">Breiman L. Random Forests // Mach. Learn. 1999. Vol. 45, № 5. P. 1–35.</mixed-citation></citation-alternatives></ref><ref id="cit14"><label>14</label><citation-alternatives><mixed-citation xml:lang="ru">Ronneberger O., Fischer P., Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation // Miccai. 2015 P. 234–241.</mixed-citation><mixed-citation xml:lang="en">Ronneberger O., Fischer P., Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation // Miccai. 2015 P. 234–241.</mixed-citation></citation-alternatives></ref></ref-list><fn-group><fn fn-type="conflict"><p>The authors declare that there are no conflicts of interest present.</p></fn></fn-group></back></article>
