Detailed Information on Publication Record
2020
When Deep Learning Meets Cell Image Synthesis
KOZUBEK, MichalBasic information
Original name
When Deep Learning Meets Cell Image Synthesis
Authors
Edition
Cytometry Part A, John Wiley & Sons, 2020, 1552-4922
Other information
Language
English
Type of outcome
Článek v odborném periodiku (nerecenzovaný)
Field of Study
10201 Computer sciences, information science, bioinformatics
Country of publisher
United States of America
Confidentiality degree
není předmětem státního či obchodního tajemství
References:
Impact factor
Impact factor: 4.355
Organization unit
Faculty of Informatics
UT WoS
000504874100001
Keywords in English
cell image synthesis; deep learning; style transfer; generative adversarial networks
Tags
Tags
International impact
Změněno: 27/4/2020 22:45, RNDr. Pavel Šmerk, Ph.D.
Abstract
V originále
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 project |
| |
| 90062, large research infrastructures |
|