Generating Synthetic MR Spectroscopic Imaging Data with Generative Adversarial Networks to Train Machine Learning Models-Reference-Cited by-同舟云学术

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Generating Synthetic MR Spectroscopic Imaging Data with Generative Adversarial Networks to Train Machine Learning Models

Published:2024 Issue: Volume: Page:
ISSN:1347-3182
Container-title:Magnetic Resonance in Medical Sciences
language:en
Short-container-title:MRMS

Author:

Maruyama Shuki¹,Takeshima Hidenori²

Affiliation:

1. Imaging Modality Group, Advanced Technology Research Department, Research and Development Center, Canon Medical Systems Corporation, Otawara, Tochigi, Japan

2. Advanced Technology Research Department, Research and Development Center, Canon Medical Systems Corporation, Kawasaki, Kanagawa, Japan

Publisher

Japanese Society for Magnetic Resonance in Medicine

Link

https://www.jstage.jst.go.jp/article/mrms/advpub/0/advpub_mp.2023-0125/_pdf

Reference52 articles.

1. 1. Sun C, Shrivastava A, Singh S, Gupta A. Revisiting unreasonable effectiveness of data in deep learning era. arXiv:1707.02968.

2. 2. Kazeminia S, Baur C, Kuijper A, et al. GANs for medical image analysis. Artif Intell Med 2020; 109:101938.

3. 3. Mzoughi H, Njeh I, Wali A, et al. Deep multi-scale 3D convolutional neural network (CNN) for MRI gliomas brain tumor classification. J Digit Imaging 2020; 33:903–915.

4. 4. Baid U, Ghodasara S, Mohan S, et al. The RSNA-ASNR-MICCAI BraTS 2021 benchmark on brain tumor segmentation and radiogenomic classification. arXiv:2107.02314.

5. 5. Amin J, Sharif M, Gul N, Yasmin M, Shad SA. Brain tumor classification based on DWT fusion of MRI sequences using convolutional neural network. Pattern Recognit Lett 2020; 129:115–122.

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