Transfer Learning in Optical Microscopy

D 2021

Transfer Learning in Optical Microscopy

KOZLOVSKÝ, Martin, David WIESNER a David SVOBODA

Základní údaje

Originální název

Transfer Learning in Optical Microscopy

Autoři

KOZLOVSKÝ, Martin (203 Česká republika, domácí), David WIESNER (203 Česká republika, domácí) a David SVOBODA (203 Česká republika, garant, domácí)

Vydání

LNCS 12965. Neuveden, Simulation and Synthesis in Medical Imaging, od s. 77-86, 10 s. 2021

Nakladatel

Springer

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Německo

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Odkazy

URL

Impakt faktor

Impact factor: 0.402 v roce 2005

Kód RIV

RIV/00216224:14330/21:00122077

Organizační jednotka

Fakulta informatiky

ISBN

978-3-030-87591-6

ISSN

DOI

http://dx.doi.org/10.1007/978-3-030-87592-3_8

UT WoS

001116104600008

Klíčová slova anglicky

Fluorescence microscopy; Phase-contrast microscopy; GAN; Image synthesis; Machine learning

Štítky

cbia-web

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 13. 5. 2024 16:28, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

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.

Návaznosti

MUNI/A/1108/2020, interní kód MU

Název: Rozsáhlé výpočetní systémy: modely, aplikace a verifikace X. (Akronym: SV-FI MAV X.)

Investor: Masarykova univerzita, Rozsáhlé výpočetní systémy: modely, aplikace a verifikace X.

Citovat

KOZLOVSKÝ, Martin, David WIESNER a David SVOBODA. Transfer Learning in Optical Microscopy. Online. In Svoboda D., Burgos N., Wolterink J., Zhao C. Simulation and Synthesis in Medical Imaging. LNCS 12965. Neuveden: Springer, 2021, s. 77-86. ISBN 978-3-030-87591-6. Dostupné z: https://dx.doi.org/10.1007/978-3-030-87592-3_8.

@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 a 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, s.~77-86. ISBN~978-3-030-87591-6. Dostupné z: https://dx.doi.org/10.1007/978-3-030-87592-3\_{}8.

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