@inproceedings{cd32e8c6ef7f4c78b856dfc0424b6653,
title = "A GAN-based data augmentation method for mitigating class imbalance problem in histopathological image classification",
abstract = "This study proposes a Generative Adversarial Network (GAN)-based data augmentation method to tackle the class imbalance issue in histopathological image classification. The proposed GAN generates high-quality images for minority classes, improving data diversity and classification performance. By integrating Global Context Attention (GCA) and UpBlock-CompRes modules, the GAN generates images that closely resemble real data, achieving superior results in FID, IS, PSNR, and SSIM compared to FastGAN. Experimental results demonstrate that the proposed GAN outperforms both the baseline without data augmentation and FastGAN, with noticeable improvements across key metrics. These enhancements highlight the effectiveness of our GAN in generating high-quality images and achieving more balanced classification results.",
keywords = "class imbalance problem, data augmentation, GAN, histopathological image classification",
author = "Lingling Yuan and Md Rahaman and Hongzan Sun and Chen Li and Yutong Gu and Tao Jiang and Marcin Grzegorzek and Xiaoyan Li",
year = "2024",
doi = "10.1109/BIBM62325.2024.10822517",
language = "English",
isbn = "9798350386233",
series = "IEEE International Conference on Bioinformatics and Biomedicine",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
pages = "5327--5334",
editor = "Mario Cannataro and Zheng, \{Huiru (Jane)\} and Lin Gao and Cheng, \{Jianlin (Jack)\} and \{de Miranda\}, \{Joao Luis\} and Ester Zumpano and Xiaohua Hu and Young-Rae Cho and Taesung Park",
booktitle = "Proceedings",
address = "United States",
note = "2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024 ; Conference date: 03-12-2024 Through 06-12-2024",
}