Zobrazeno 1 - 10
of 80
pro vyhledávání: '"Ivan Cimrák"'
Autor:
Ivan Cimrak, Katarina Bachrata, Hynek Bachraty, Iveta Jancigova, Renata Tothova, Martin Busik, Martin Slavik, Markus Gusenbauer
Publikováno v:
Communications, Vol 18, Iss 1A, Pp 13-20 (2016)
We present a fully three-dimensional computational model of red blood cells and their flow in a fluid. This model includes all components necessary to capture important physical and biological aspects of the cell flow. It comprises descriptions of el
Externí odkaz:
https://doaj.org/article/28c91afb7789498b838261cf093d6943
Publikováno v:
Data, Vol 9, Iss 8, p 100 (2024)
In this work, we describe a dataset suitable for analyzing the extent to which hematoxylin–eosin (HE)-stained tissue contains information about the expression of Ki67 in immunohistochemistry staining. The dataset provides images of corresponding pa
Externí odkaz:
https://doaj.org/article/86c5edcbd4094f7fb14052cc212b7935
Publikováno v:
Data, Vol 8, Iss 6, p 106 (2023)
This work presents a dataset comprising images, annotations, and velocity fields for benchmarking cell detection and cell tracking algorithms. The dataset includes two video sequences captured during laboratory experiments, showcasing the flow of red
Externí odkaz:
https://doaj.org/article/65157542394241e1bbda78b87b5db8d8
Publikováno v:
Journal of Imaging, Vol 9, Iss 5, p 95 (2023)
Deep neural networks have gained popularity in the field of mammography. Data play an integral role in training these models, as training algorithms requires a large amount of data to capture the general relationship between the model’s input and o
Externí odkaz:
https://doaj.org/article/01cb31bcfee84a72a0f092458e01394b
Autor:
Dominika Petríková, Ivan Cimrák
Publikováno v:
Computation, Vol 11, Iss 4, p 81 (2023)
Deep learning (DL) and convolutional neural networks (CNNs) have achieved state-of-the-art performance in many medical image analysis tasks. Histopathological images contain valuable information that can be used to diagnose diseases and create treatm
Externí odkaz:
https://doaj.org/article/df72631d86d64811855ac6f22fc9afc3
Publikováno v:
Applied Sciences, Vol 12, Iss 8, p 3859 (2022)
The sorting of biological cells or particles immersed in a fluid is a frequent goal in the domain of microfluidics. One approach for such sorting is in using the inertial effects that are present in curved channels. In this study, we propose a new ap
Externí odkaz:
https://doaj.org/article/1384130c6c4849e6a19fec5e84703c5e
Publikováno v:
PLoS Computational Biology, Vol 16, Iss 10, p e1008249 (2020)
A user ready, well documented software package PyOIF contains an implementation of a robust validated computational model for cell flow modelling. The software is capable of simulating processes involving biological cells immersed in a fluid. The exa
Externí odkaz:
https://doaj.org/article/7ed6a3e9783245cfb683a5c5b5b340f8
Publikováno v:
Micromachines, Vol 12, Iss 8, p 974 (2021)
The inner viscosity of a biological red blood cell is about five times larger than the viscosity of the blood plasma. In this work, we use dissipative particles to enable the proper viscosity contrast in a mesh-based red blood cell model. Each soft p
Externí odkaz:
https://doaj.org/article/922b6f2e20fa44bdbff9a327cc61e2c3
Publikováno v:
Computational Particle Mechanics.
The studies that compare the metastatic potential of tumor cell clusters in microcirculation to that of single tumor cells show that the clusters contribute significantly to metastasizing. The metastatic potential is conditioned by the presence of th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8bdfdf1c8d0cb414b13dda476ddb67dc
https://doi.org/10.1007/s40571-022-00533-2
https://doi.org/10.1007/s40571-022-00533-2
Autor:
Alžbeta Bugáňová, Ivan Cimrák
Publikováno v:
Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ef3334b5b5cce58997b683661c5db1a6
https://doi.org/10.5220/0011788200003414
https://doi.org/10.5220/0011788200003414