Data Augmentation Using Generative Adversarial Networks for Multi-Class Segmentation of Lung Confoca...
Data Augmentation Using Generative Adversarial Networks for Multi-Class Segmentation of Lung Confocal IF Images
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Full title
Author / Creator
Division of Biomedical Informatics, Cincinnati Childrens Hospital Medical Center 3333 Burnet Aveue, Cincinnati, OH 45229, USA , Graduate School of Engineering, Mie University 1577 Kurima-machiya, Tsu, Mie 514-8507, Japan , Department of Pediatrics, University of Cincinnati College of Medicine Cincinnati, OH 45257, USA , Katsuma, Daiki , Kawanaka, Hiroharu , Prasath, V. B. Surya and Aronow, Bruce J.
Publisher
Tokyo: Fuji Technology Press Co. Ltd
Journal title
Language
English
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Publication information
Publisher
Tokyo: Fuji Technology Press Co. Ltd
Subjects
More information
Scope and Contents
Contents
The human lung is a complex organ with high cellular heterogeneity, and its development and maintenance require interactive gene networks and dynamic cross-talk among multiple cell types. We focus on the confocal immunofluorescent (IF) images of lung tissues from the LungMAP database to reveal lung development. Using the current state-of-the-art de...
Alternative Titles
Full title
Data Augmentation Using Generative Adversarial Networks for Multi-Class Segmentation of Lung Confocal IF Images
Authors, Artists and Contributors
Author / Creator
Graduate School of Engineering, Mie University 1577 Kurima-machiya, Tsu, Mie 514-8507, Japan
Department of Pediatrics, University of Cincinnati College of Medicine Cincinnati, OH 45257, USA
Katsuma, Daiki
Kawanaka, Hiroharu
Prasath, V. B. Surya
Aronow, Bruce J.
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_proquest_journals_2640559078
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2640559078
Other Identifiers
ISSN
1343-0130
E-ISSN
1883-8014
DOI
10.20965/jaciii.2022.p0138
