Enhancing Medical Imaging with GANs Synthesizing Realistic Images from Limited Data
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866916303333228544 |
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| author | Feng, Yinqiu Zhang, Bo Xiao, Lingxi Yang, Yutian Gegen, Tana Chen, Zexi |
| author_facet | Feng, Yinqiu Zhang, Bo Xiao, Lingxi Yang, Yutian Gegen, Tana Chen, Zexi |
| contents | In this research, we introduce an innovative method for synthesizing medical images using generative adversarial networks (GANs). Our proposed GANs method demonstrates the capability to produce realistic synthetic images even when trained on a limited quantity of real medical image data, showcasing commendable generalization prowess. To achieve this, we devised a generator and discriminator network architecture founded on deep convolutional neural networks (CNNs), leveraging the adversarial training paradigm for model optimization. Through extensive experimentation across diverse medical image datasets, our method exhibits robust performance, consistently generating synthetic images that closely emulate the structural and textural attributes of authentic medical images. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_18547 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Medical Imaging with GANs Synthesizing Realistic Images from Limited Data Feng, Yinqiu Zhang, Bo Xiao, Lingxi Yang, Yutian Gegen, Tana Chen, Zexi Image and Video Processing Computer Vision and Pattern Recognition In this research, we introduce an innovative method for synthesizing medical images using generative adversarial networks (GANs). Our proposed GANs method demonstrates the capability to produce realistic synthetic images even when trained on a limited quantity of real medical image data, showcasing commendable generalization prowess. To achieve this, we devised a generator and discriminator network architecture founded on deep convolutional neural networks (CNNs), leveraging the adversarial training paradigm for model optimization. Through extensive experimentation across diverse medical image datasets, our method exhibits robust performance, consistently generating synthetic images that closely emulate the structural and textural attributes of authentic medical images. |
| title | Enhancing Medical Imaging with GANs Synthesizing Realistic Images from Limited Data |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.18547 |