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Automatic assessment of the cardiomyocyte development stages from confocal microscopy images using deep convolutional networks
Title Automatic assessment of the cardiomyocyte development stages from confocal microscopy images using deep convolutional networks Author Škrabánek Pavel Co-authors Zahradníková Alexandra 1975- SAVMOFYZ ; SAVCEBIO ; SAVEXEND ; SAVBIOMED - Ústav molekulárnej fyziológie a genetiky SAV RID ORCID Source document PLoS ONE. Vol. 14, iss. 5 (2019), p. e0216720 Language eng - English Document kind rozpis článkov z periodík (rbx) Citations LI, Yu-Jie - ZHANG, Li-Ge - ZHI, Hong-Yu - ZHONG, Kun-Hua - HE, Wen-Quan - CHEN, Yang - YANG, Zhi-Yong - CHEN, Lin - BAI, Xue-Hong - QIN, Xiao-Lin - LI, Dan-Feng - WANG, Dan-Dan - GU, Jian-Teng - NING, Jiao-Lin - LU, Kai-Zhi - ZHANG, Ju - XIA, Zheng-Yuan - CHEN, Yu-Wen - YI, Bin. A better method for the dynamic, precise estimating of blood/haemoglobin loss based on deep learning of artificial intelligence. In ANNALS OF TRANSLATIONAL MEDICINE. ISSN 2305-5839, 2020, vol. 8, no. 19, pp. GLASS, Carolyn - LAFATA, Kyle J. - JECK, William - HORSTMEYER, Roarke - COOKE, Colin - EVERITT, Jeffrey - GLASS, Matthew - DOV, David - SEIDMAN, Michael A. The Role of Machine Learning in Cardiovascular Pathology. In CANADIAN JOURNAL OF CARDIOLOGY. ISSN 0828-282X, 2022, vol. 38, no. 2, pp. 234-245. Dostupné na: https://doi.org/10.1016/j.cjca.2021.11.008. Category ADMA - Scientific papers in foreign impacted journals registered in Web of Sciences or Scopus Category of document (from 2022) V3 - Vedecký výstup publikačnej činnosti z časopisu Type of document článok Year 2019 Registered in WOS Registered in SCOPUS DOI 10.1371/journal.pone.0216720 
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File name Access Size Downloaded Type License Automatic assessment of the cardiomyocyte development stages from confocal microscopy images using deep convolutional networks.pdf available 7.1 MB 0 Publisher's version rok CC IF IF Q (best) JCR Av Jour IF Perc SJR SJR Q (best) CiteScore N rok vydania rok metriky IF IF Q (best) SJR SJR Q (best) 2019 2018 2.776 Q2 1.100 Q1
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