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@article{1633798,
author = {Kozubek, Michal},
article_number = {3},
doi = {http://dx.doi.org/10.1002/cyto.a.23957},
keywords = {cell image synthesis; deep learning; style transfer; generative adversarial networks},
language = {eng},
issn = {1552-4922},
journal = {Cytometry Part A},
title = {When Deep Learning Meets Cell Image Synthesis},
url = {https://doi.org/10.1002/cyto.a.23957},
volume = {97},
year = {2020}
}
TY - JFULL
ID - 1633798
AU - Kozubek, Michal
PY - 2020
TI - When Deep Learning Meets Cell Image Synthesis
JF - Cytometry Part A
VL - 97
IS - 3
SP - 222-225
EP - 222-225
PB - John Wiley & Sons
SN - 15524922
KW - cell image synthesis
KW - deep learning
KW - style transfer
KW - generative adversarial networks
UR - https://doi.org/10.1002/cyto.a.23957
N2 - Deep learning methods developed by the computer vision community are successfully being adapted for use in biomedical image analysis and synthesis applications with some delay. Also in cell image synthesis, we can observe significant improvements in the quality of generated results brought about by deep learning. The typical task is to generate isolated cell images based on training image examples with cropped, centered, and aligned individual cells. While the first trials to use generative adversarial networks (GANs) without any object detection or segmentation had limited capabilities, the recent article by Scalbert et al. 1 has shown that significant improvement can be obtained by splitting the task into (1) learning and generating object (cell and/or nuclei) shapes based on image segmentation, and (2) learning and generating the texture separately for each segment type including the background using so‐called style transfer.
ER -
KOZUBEK, Michal. When Deep Learning Meets Cell Image Synthesis. \textit{Cytometry Part A}. John Wiley \&{} Sons, 2020, roč.~97, č.~3, s.~222-225. ISSN~1552-4922. Dostupné z: https://dx.doi.org/10.1002/cyto.a.23957.
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