The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Les images médicales jouent un rôle important dans le diagnostic médical. Cependant, acquérir un grand nombre de jeux de données annotés reste une tâche difficile dans le domaine médical. Pour cette raison, la recherche dans le domaine de la traduction d’image à image est combinée au diagnostic assisté par ordinateur, et des méthodes d’augmentation des données basées sur des réseaux contradictoires génératifs sont appliquées aux images médicales. Dans cet article, nous essayons d'effectuer une augmentation de données sur des données unimodales. Le réseau conçu basé sur StarGAN V2 présente de hautes performances pour augmenter l'ensemble de données à l'aide d'un petit nombre d'images originales, et les données augmentées sont étendues des données unimodales aux images médicales multimodales, et ces données d'images médicales multimodales peuvent être appliquées à la tâche de segmentation avec certains amélioration des résultats de segmentation. Nos expériences démontrent que les données d'images médicales multimodales générées peuvent améliorer les performances de segmentation des gliomes.
Yue PENG
Guangxi University
Zuqiang MENG
Guangxi University
Lina YANG
Guangxi University
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Yue PENG, Zuqiang MENG, Lina YANG, "Image-to-Image Translation for Data Augmentation on Multimodal Medical Images" in IEICE TRANSACTIONS on Information,
vol. E106-D, no. 5, pp. 686-696, May 2023, doi: 10.1587/transinf.2022DLP0008.
Abstract: Medical images play an important role in medical diagnosis. However, acquiring a large number of datasets with annotations is still a difficult task in the medical field. For this reason, research in the field of image-to-image translation is combined with computer-aided diagnosis, and data augmentation methods based on generative adversarial networks are applied to medical images. In this paper, we try to perform data augmentation on unimodal data. The designed StarGAN V2 based network has high performance in augmenting the dataset using a small number of original images, and the augmented data is expanded from unimodal data to multimodal medical images, and this multimodal medical image data can be applied to the segmentation task with some improvement in the segmentation results. Our experiments demonstrate that the generated multimodal medical image data can improve the performance of glioma segmentation.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022DLP0008/_p
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@ARTICLE{e106-d_5_686,
author={Yue PENG, Zuqiang MENG, Lina YANG, },
journal={IEICE TRANSACTIONS on Information},
title={Image-to-Image Translation for Data Augmentation on Multimodal Medical Images},
year={2023},
volume={E106-D},
number={5},
pages={686-696},
abstract={Medical images play an important role in medical diagnosis. However, acquiring a large number of datasets with annotations is still a difficult task in the medical field. For this reason, research in the field of image-to-image translation is combined with computer-aided diagnosis, and data augmentation methods based on generative adversarial networks are applied to medical images. In this paper, we try to perform data augmentation on unimodal data. The designed StarGAN V2 based network has high performance in augmenting the dataset using a small number of original images, and the augmented data is expanded from unimodal data to multimodal medical images, and this multimodal medical image data can be applied to the segmentation task with some improvement in the segmentation results. Our experiments demonstrate that the generated multimodal medical image data can improve the performance of glioma segmentation.},
keywords={},
doi={10.1587/transinf.2022DLP0008},
ISSN={1745-1361},
month={May},}
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TY - JOUR
TI - Image-to-Image Translation for Data Augmentation on Multimodal Medical Images
T2 - IEICE TRANSACTIONS on Information
SP - 686
EP - 696
AU - Yue PENG
AU - Zuqiang MENG
AU - Lina YANG
PY - 2023
DO - 10.1587/transinf.2022DLP0008
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E106-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2023
AB - Medical images play an important role in medical diagnosis. However, acquiring a large number of datasets with annotations is still a difficult task in the medical field. For this reason, research in the field of image-to-image translation is combined with computer-aided diagnosis, and data augmentation methods based on generative adversarial networks are applied to medical images. In this paper, we try to perform data augmentation on unimodal data. The designed StarGAN V2 based network has high performance in augmenting the dataset using a small number of original images, and the augmented data is expanded from unimodal data to multimodal medical images, and this multimodal medical image data can be applied to the segmentation task with some improvement in the segmentation results. Our experiments demonstrate that the generated multimodal medical image data can improve the performance of glioma segmentation.
ER -