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National Repository of Grey Literature 3 records found  Search took 0.01 seconds. 
Benchmark of the Computational Tools for the Prediction of the Effect of Mutations on Protein Stability
 Berezný, Matej ; Martínek, Tomáš (referee) ; Musil, Miloš (advisor)
Návrh proteínov vyžaduje informáciu o tom ako mutácie ovplyvňujú celkovú stabilitu proteinu. Pre tento prípad existuje mnoho verejne dostupných nástrojov avšak ich kolektívne používanie či porovnávanie je veľmi pracné. Presne pre tento prípad som vyvinul BenchStab; konzolovú aplikáciu/Python knižnicu navrhnutú pre rýchlu a priamočiaru manipuláciu s 18 prediktormi, umožňujúc hromadné získavanie mutačných výsledkov. Zároveň som vytvoril novú unikátnu dátovú sadu, získanú z FireProtDB. Tento dataset som použil na porovnanie 24 rôznych predikčných metód pomocou rôznych metrík.
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Volumetric Segmentation of Dental CT Data
 Berezný, Matej ; Kodym, Oldřich (referee) ; Čadík, Martin (advisor)
The main goal of this work was to use neural networks for volumetric segmentation of dental CBCT data. As a byproducts, both new dataset including sparse and dense annotations and automatic preprocessing pipeline were produced. Additionally, the possibility of applying transfer learning and multi-phase training in order to improve segmentation results was tested. From the various tests that were carried out, conclusion can be drawn that both multi-phase training and transfer learning showed substantial improvement in dice score for both sparse and dense annotations compared to the baseline method.
Detailed record
Volumetric Segmentation of Dental CT Data
 Berezný, Matej ; Kodym, Oldřich (referee) ; Čadík, Martin (advisor)
The main goal of this work was to use neural networks for volumetric segmentation of dental CBCT data. As a byproducts, both new dataset including sparse and dense annotations and automatic preprocessing pipeline were produced. Additionally, the possibility of applying transfer learning and multi-phase training in order to improve segmentation results was tested. From the various tests that were carried out, conclusion can be drawn that both multi-phase training and transfer learning showed substantial improvement in dice score for both sparse and dense annotations compared to the baseline method.
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