IEICE TRANSACTIONS on Information
Image-to-Image Translation for Data Augmentation on Multimodal Medical Images
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E106-D No.5 pp.686-696
- Publication Date
- 2023/05/01
- Publicized
- 2022/03/01
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2022DLP0008
- Type of Manuscript
- Special Section PAPER (Special Section on Deep Learning Technologies: Architecture, Optimization, Techniques, and Applications)
- Category
- Smart Healthcare
Authors
Yue PENG
Guangxi University
Zuqiang MENG
Guangxi University
Lina YANG
Guangxi University
Keyword
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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},}
Copy
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 -
English
