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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2311.14388 |
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| _version_ | 1866912087118184448 |
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| author | Xiong, Xiangyu Sun, Yue Liu, Xiaohong Lam, Chan-Tong Tong, Tong Chen, Hao Gao, Qinquan Ke, Wei Tan, Tao |
| author_facet | Xiong, Xiangyu Sun, Yue Liu, Xiaohong Lam, Chan-Tong Tong, Tong Chen, Hao Gao, Qinquan Ke, Wei Tan, Tao |
| contents | Although current data augmentation methods are successful to alleviate the data insufficiency, conventional augmentation are primarily intra-domain while advanced generative adversarial networks (GANs) generate images remaining uncertain, particularly in small-scale datasets. In this paper, we propose a parameterized GAN (ParaGAN) that effectively controls the changes of synthetic samples among domains and highlights the attention regions for downstream classification. Specifically, ParaGAN incorporates projection distance parameters in cyclic projection and projects the source images to the decision boundary to obtain the class-difference maps. Our experiments show that ParaGAN can consistently outperform the existing augmentation methods with explainable classification on two small-scale medical datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_14388 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Parameterized Generative Adversarial Network Using Cyclic Projection for Explainable Medical Image Classification Xiong, Xiangyu Sun, Yue Liu, Xiaohong Lam, Chan-Tong Tong, Tong Chen, Hao Gao, Qinquan Ke, Wei Tan, Tao Computer Vision and Pattern Recognition Machine Learning Although current data augmentation methods are successful to alleviate the data insufficiency, conventional augmentation are primarily intra-domain while advanced generative adversarial networks (GANs) generate images remaining uncertain, particularly in small-scale datasets. In this paper, we propose a parameterized GAN (ParaGAN) that effectively controls the changes of synthetic samples among domains and highlights the attention regions for downstream classification. Specifically, ParaGAN incorporates projection distance parameters in cyclic projection and projects the source images to the decision boundary to obtain the class-difference maps. Our experiments show that ParaGAN can consistently outperform the existing augmentation methods with explainable classification on two small-scale medical datasets. |
| title | A Parameterized Generative Adversarial Network Using Cyclic Projection for Explainable Medical Image Classification |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2311.14388 |