Národní úložiště šedé literatury Nalezeno 2 záznamů.  Hledání trvalo 0.00 vteřin. 
Segmentation of biological samples in cryo-electron microscopy images using machine learning methods
Sokol, Norbert ; Vičar, Tomáš (oponent) ; Chmelík, Jiří (vedoucí práce)
Cryo-electron microscopy imaging has its irreplaceable position in analysis of various biological structures. Localization of the cells cultivated on grid and their segmentation towards background or contamination is essential. With the development of various deep learning methods, the performance of semantic segmentation tasks dramatically increased. In this thesis, we will develop a deep convolutional neural network for semantic segmentation of the cells cultivated on grid. Dataset for this thesis was created with dual-beam cryo-electron microscope developed by Thermo Fisher Scientific Brno.
Segmentation of biological samples in cryo-electron microscopy images using machine learning methods
Sokol, Norbert ; Vičar, Tomáš (oponent) ; Chmelík, Jiří (vedoucí práce)
Cryo-electron microscopy imaging has its irreplaceable position in analysis of various biological structures. Localization of the cells cultivated on grid and their segmentation towards background or contamination is essential. With the development of various deep learning methods, the performance of semantic segmentation tasks dramatically increased. In this thesis, we will develop a deep convolutional neural network for semantic segmentation of the cells cultivated on grid. Dataset for this thesis was created with dual-beam cryo-electron microscope developed by Thermo Fisher Scientific Brno.

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