Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: Naoya Wada and Masaya Kobayashi

Affiliation: KYOCERA Corporation, 3-7-1 Minatomirai Nishi-ku, Yokohama, Japan

Keyword(s): Generative Adversarial Networks (Gans), Image-to-Image Translation, Domain Adaptation, Unsupervised Learning, MRI.

Abstract: In this paper, a new domain adaptation technique is presented for image-to-image translation into the real-world color domain. Although CycleGAN has become a standard technique for image translation without pairing images to train the network, it is not able to adapt the domain of the generated image to small domains such as color and illumination. Other techniques require large datasets for training. In our technique, two source images are introduced: one for image translation and another for color adaptation. Color adaptation is realized by introducing color histograms to the two generators in CycleGAN and estimating losses for color. Experiments using simulated images based on the OsteoArthritis Initiative MRI dataset show promising results in terms of color difference and image comparisons.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.147.49.219

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Wada, N. and Kobayashi, M. (2022). Unsupervised Image-to-Image Translation from MRI-based Simulated Images to Realistic Images Reflecting Specific Color Characteristics. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - BIOIMAGING; ISBN 978-989-758-552-4; ISSN 2184-4305, SciTePress, pages 184-189. DOI: 10.5220/0010916300003123

@conference{bioimaging22,
author={Naoya Wada and Masaya Kobayashi},
title={Unsupervised Image-to-Image Translation from MRI-based Simulated Images to Realistic Images Reflecting Specific Color Characteristics},
booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - BIOIMAGING},
year={2022},
pages={184-189},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010916300003123},
isbn={978-989-758-552-4},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - BIOIMAGING
TI - Unsupervised Image-to-Image Translation from MRI-based Simulated Images to Realistic Images Reflecting Specific Color Characteristics
SN - 978-989-758-552-4
IS - 2184-4305
AU - Wada, N.
AU - Kobayashi, M.
PY - 2022
SP - 184
EP - 189
DO - 10.5220/0010916300003123
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic