Noise Adaptation Generative Adversarial Network for Medical Image Analysis
文献类型：期刊论文
| 作者 | Zhang, Tianyang; Cheng, Jun; Fu, Huazhu; Gu, Zaiwang; Xiao, Yuting; Zhou, Kang; Gao, Shenghua; Zheng, Rui; Liu, Jiang |
| 刊名 | IEEE TRANSACTIONS ON MEDICAL IMAGING
 |
| 出版日期 | 2020 |
| 卷号 | 39期号:4页码:1149-1159 |
| DOI | 10.1109/TMI.2019.2944488 |
| 英文摘要 | Machine learning has been widely used in medical image analysis under an assumption that the training and test data are under the same feature distributions. However, medical images from difference devices or the same device with different parameter settings are often contaminated with different amount and types of noises, which violate the above assumption. Therefore, the models trained using data from one device or setting often fail to work for that from another. Moreover, it is very expensive and tedious to label data and re-train models for all different devices or settings. To overcome this noise adaptation issue, it is necessary to leverage on the models trained with data from one device or setting for new data. In this paper, we reformulate this noise adaptation task as an image-to-image translation task such that the noise patterns from the test data are modified to be similar to those from the training data while the contents of the data are unchanged. In this paper, we propose a novel Noise Adaptation Generative Adversarial Network (NAGAN), which contains a generator and two discriminators. The generator aims to map the data from source domain to target domain. Among the two discriminators, one discriminator enforces the generated images to have the same noise patterns as those from the target domain, and the second discriminator enforces the content to be preserved in the generated images. We apply the proposed NAGAN on both optical coherence tomography (OCT) images and ultrasound images. Results show that the method is able to translate the noise style. In addition, we also evaluate our proposed method with segmentation task in OCT and classification task in ultrasound. The experimental results show that the proposed NAGAN improves the analysis outcome. |
| 学科主题 | Computer Science ; Engineering ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging |
| 源URL | [http://ir.nimte.ac.cn/handle/174433/19826]  |
| 专题 | 2020专题 2020专题_期刊论文 |
| 作者单位 | Cheng, J (corresponding author), Chinese Acad Sci, Cixi Inst Biomed Engn, Ningbo Inst Ind Technol, Ningbo 315201, Peoples R China. |
| 推荐引用方式 GB/T 7714 | Zhang, Tianyang,Cheng, Jun,Fu, Huazhu,et al. Noise Adaptation Generative Adversarial Network for Medical Image Analysis[J]. IEEE TRANSACTIONS ON MEDICAL IMAGING,2020,39(4):1149-1159. |
| APA | Zhang, Tianyang.,Cheng, Jun.,Fu, Huazhu.,Gu, Zaiwang.,Xiao, Yuting.,...&Liu, Jiang.(2020).Noise Adaptation Generative Adversarial Network for Medical Image Analysis.IEEE TRANSACTIONS ON MEDICAL IMAGING,39(4),1149-1159. |
| MLA | Zhang, Tianyang,et al."Noise Adaptation Generative Adversarial Network for Medical Image Analysis".IEEE TRANSACTIONS ON MEDICAL IMAGING 39.4(2020):1149-1159. |
入库方式： OAI收割
来源：宁波材料技术与工程研究所
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