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A Parameterized Generative Adversarial Network Using Cyclic Projection for Explainable Medical Image Classification

DOI:
10.60864/yw8s-en84
Citation Author(s):
Xiangyu Xiong, Yue Sun, Xiaohong Liu, Chan-Tong Lam, Tong Tong, Hao Chen, Qinquan Gao, Wei Ke, Tao Tan
Submitted by:
xiangyu xiong
Last updated:
6 June 2024 - 10:28am
Document Type:
Poster
Document Year:
2024
Event:
Presenters:
Xiangyu Xiong
Paper Code:
https://github.com/yXiangXiong/ParaGAN
 

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.

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