Other formats:
BibTeX
LaTeX
RIS
@inproceedings{1785739,
author = {Kozlovský, Martin and Wiesner, David and Svoboda, David},
address = {Neuveden},
booktitle = {Simulation and Synthesis in Medical Imaging},
doi = {http://dx.doi.org/10.1007/978-3-030-87592-3_8},
edition = {LNCS 12965},
editor = {Svoboda D., Burgos N., Wolterink J., Zhao C.},
keywords = {Fluorescence microscopy; Phase-contrast microscopy; GAN; Image synthesis; Machine learning},
howpublished = {elektronická verze "online"},
language = {eng},
location = {Neuveden},
isbn = {978-3-030-87591-6},
pages = {77-86},
publisher = {Springer},
title = {Transfer Learning in Optical Microscopy},
url = {https://2021.sashimi-workshop.org/},
year = {2021}
}
TY - JOUR
ID - 1785739
AU - Kozlovský, Martin - Wiesner, David - Svoboda, David
PY - 2021
TI - Transfer Learning in Optical Microscopy
PB - Springer
CY - Neuveden
SN - 9783030875916
KW - Fluorescence microscopy
KW - Phase-contrast microscopy
KW - GAN
KW - Image synthesis
KW - Machine learning
UR - https://2021.sashimi-workshop.org/
N2 - Image synthesis is nowadays a very rapidly evolving branch of deep learning. One of possible applications of image synthesis is an image-to-image translation. There is currently a lot of focus orientated to applications of image translation in medicine, mainly involving translation between different screening techniques. One of other possible use of image translation in medicine and biology is in the task of translation between various imaging techniques in modern microscopy. In this paper, we propose a novel method based on DenseNet architecture and we compare it with Pix2Pix model in the task of translation from images imaged using phase-contrast technique to fluorescence images with focus on usability for cell segmentation.
ER -
KOZLOVSKÝ, Martin, David WIESNER and David SVOBODA. Transfer Learning in Optical Microscopy. Online. In Svoboda D., Burgos N., Wolterink J., Zhao C. \textit{Simulation and Synthesis in Medical Imaging}. LNCS 12965. Neuveden: Springer, 2021, p.~77-86. ISBN~978-3-030-87591-6. Available from: https://dx.doi.org/10.1007/978-3-030-87592-3\_{}8.
|
