Distance Guided Generative Adversarial Network for Explainable Binary Classifications

1 code implementation • 29 Dec 2023 • Xiangyu Xiong, Yue Sun, Xiaohong Liu, Wei Ke, Chan-Tong Lam, Jiangang Chen, Mingfeng Jiang, Mingwei Wang, Hui Xie, Tong Tong, Qinquan Gao, Hao Chen, Tao Tan

Experimental results show that DisGAN consistently outperforms the GAN-based augmentation methods with explainable binary classification.

Binary Classification Classification +3

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

no code implementations • 24 Nov 2023 • Xiangyu Xiong, Yue Sun, Xiaohong Liu, Chan-Tong Lam, Tong Tong, Hao Chen, Qinquan Gao, Wei Ke, Tao Tan

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.

Data Augmentation Generative Adversarial Network +2

A Cascaded Multi-Task Generative Framework for Detecting Aortic Dissection on 3D Non-contrast-enhanced Computed Tomography

1 code implementation • journal 2022 • Xiangyu Xiong, Yan Ding, Chuanqi Sun, Zhuoneng Zhang, Xiuhong Guan, Tianjing Zhang, Hao Chen, Hongyan Liu, Zhangbo Cheng, Lei Zhao, Xiaohai Ma, Guoxi Xie

Experiments also showed that the proposed framework could achieve an average accuracy of 0. 831, a sensitivity of 0. 938, and an F1-score of 0. 847 in comparison with seven state-of-the-art classification models used by three radiologists with junior, intermediate, and senior experiences, respectively.