KOZUBEK, Michal. When Deep Learning Meets Cell Image Synthesis. Cytometry Part A. John Wiley & Sons, vol. 97, No 3, p. 222-225. ISSN 1552-4922. doi:10.1002/cyto.a.23957. 2020.
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Basic information
Original name When Deep Learning Meets Cell Image Synthesis
Authors KOZUBEK, Michal.
Edition Cytometry Part A, John Wiley & Sons, 2020, 1552-4922.
Other information
Original language English
Type of outcome Article in a journal (not reviewed)
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher United States of America
Confidentiality degree is not subject to a state or trade secret
WWW URL
Impact factor Impact factor: 4.355
Organization unit Faculty of Informatics
Doi http://dx.doi.org/10.1002/cyto.a.23957
UT WoS 000504874100001
Keywords in English cell image synthesis; deep learning; style transfer; generative adversarial networks
Tags cbia-web
Tags International impact
Changed by Changed by: RNDr. Pavel Šmerk, Ph.D., učo 3880. Changed: 27/4/2020 22:45.
Abstract
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.
Links
EF16_013/0001775, research and development projectName: Modernizace a podpora výzkumných aktivit národní infrastruktury pro biologické a medicínské zobrazování Czech-BioImaging
90062, large research infrastructuresName: Czech-BioImaging
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