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Updated: Jun 10, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

远程引导的生成对抗网络用于可解释的医学图像分类.

Xiangyu Xiong¹, Yue Sun¹, Xiaohong Liu²

¹Faculty of Applied Sciences, Macao Polytechnic University, 999078, Macao Special Administrative Region of China.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society

|October 19, 2024

概括

本研究介绍了远程引导的生成对抗网络 (DisGAN),以改进对二进制分类的数据增强. 迪斯甘增强了样本的多样性,并澄清了决策边界,优于现有的方法.

关键词:

二元分类二元分类二元分类.数据增强数据增强决定边界边界边界可以解释的可解释性.生成性的对抗性网络.超平面飞机是一种超平面飞机.

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科学领域:

计算机科学计算机科学
人工智能的人工智能
机器学习机器学习

背景情况:

传统的数据增强依赖于域内知识,限制了其有效性.
现有的生成对抗网络 (GAN) 提供有限的跨域样本多样性.
当前的方法对二进制分类任务的决策边界描述不充分.

研究的目的:

为增强数据增强提出一种新的远程引导GAN (DisGAN).
改进二进制分类中决策边界的描述.
为了产生更多样化的域间和域内样本.

主要方法:

通过将垂直距离GAN (VerDisGAN) 和水平距离GAN (HorDisGAN) 结合起来,开发了DisGAN.
VerDisGAN 在垂直距离上条件域间生成.
HorDisGAN条件在水平距离上生成域内生成,为特定类区域将源图像映射到超平面.

主要成果:

在可解释的二进制分类中,DisGAN始终优于现有的基于GAN的增强方法.
提出的方法在澄清决策边界方面表现出卓越的表现.
VerDisGAN有效地通过将图像映射到超平面上来产生类特定区域.

结论:

在二进制分类数据增强方面,DisGAN提供了显著的进步.
该方法通过改进决策边界描述来提供可解释的分类.
DisGAN适用于各种分类架构,并显示了多类扩展的潜力.