@inproceedings{43b0e2c3aebc4197b5f8616ba29e611e,
title = "A PARAMETERIZED GENERATIVE ADVERSARIAL NETWORK USING CYCLIC PROJECTION FOR EXPLAINABLE MEDICAL IMAGE CLASSIFICATIONS",
abstract = "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.",
keywords = "Data augmentation, explainable classification, parameterized generative adversarial network, projection distance, small-scale datasets",
author = "Xiangyu Xiong and Yue Sun and Xiaohong Liu and Lam, {Chan Tong} and Tong Tong and Hao Chen and Qinquan Gao and Wei Ke and Tao Tan",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 49th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024 ; Conference date: 14-04-2024 Through 19-04-2024",
year = "2024",
doi = "10.1109/ICASSP48485.2024.10448260",
language = "English",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "7310--7314",
booktitle = "2024 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024 - Proceedings",
address = "United States",
}