GAN review: Models and medical image fusion applications

鉴别器 计算机科学 领域(数学) 编码器 生成语法 人工智能 人工神经网络 卷积神经网络 发电机(电路理论) 深度学习 电信 数学 操作系统 探测器 物理 功率(物理) 纯数学 量子力学
作者
Tao Zhou,Qi Li,Huiling Lu,Qianru Cheng,Xiangxiang Zhang
出处
期刊:Information Fusion [Elsevier BV]
卷期号:91: 134-148 被引量:136
标识
DOI:10.1016/j.inffus.2022.10.017
摘要

Generative Adversarial Network (GAN) is a research hotspot in deep generative models, which has been widely used in the field of medical image fusion. This paper summarizes GAN models from the following four aspects: firstly, the basic principles of GAN are expounded from two aspects: basic model and training process; secondly, variant GAN models are summarized into three directions (Probability Distribution Distance, Overall Network Architecture, Neural Network Structure), from the methods based on f-divergence, the methods based on IPM, Single-Generator and Dual-Discriminators GAN, Multi-Generators and Single-Discriminator GAN, Multi-Generators and Multi-Discriminators GAN, Conditional Constraint GAN, Convolutional Neural Network structure GAN and Auto-Encoder Neural Network structure GAN are eight dimensions to summarize the typical models in recent years; thirdly, the advantages and application of GAN models in the field of medical image fusion are explored from three aspects; fourthly, the main challenges faced by GAN and the challenges faced by GAN models in medical image fusion field are discussed and the future prospects are given. This paper systematically summarizes various models of GAN, advantages and challenges of GAN models in medical image fusion field, which is very important for the future research of GAN.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
刚刚
joseph应助半糖糖采纳,获得10
1秒前
席松发布了新的文献求助10
1秒前
乐乐应助欧欧拉格朗日采纳,获得10
1秒前
小L发布了新的文献求助10
2秒前
王几几发布了新的文献求助10
3秒前
zhouz完成签到,获得积分10
3秒前
星渊完成签到,获得积分10
5秒前
6秒前
7秒前
慢羊羊发布了新的文献求助10
7秒前
CipherSage应助yuany采纳,获得10
8秒前
Janisa完成签到,获得积分10
9秒前
cczltdy完成签到,获得积分10
10秒前
宋66发布了新的文献求助20
12秒前
hiyuz完成签到,获得积分10
12秒前
hhh完成签到 ,获得积分10
13秒前
归尘应助欧欧拉格朗日采纳,获得10
14秒前
席松完成签到,获得积分10
15秒前
卡卡西应助失忆的蝴蝶采纳,获得20
15秒前
water应助zheng能量采纳,获得10
17秒前
20秒前
英俊的铭应助hiyuz采纳,获得10
21秒前
22秒前
yuany发布了新的文献求助10
23秒前
乔乔兔应助严艾采纳,获得20
24秒前
abc发布了新的文献求助10
24秒前
干饭大王应助liuzhen采纳,获得10
26秒前
香蕉觅云应助吃吃采纳,获得10
26秒前
27秒前
27秒前
情怀应助叶叶叶采纳,获得10
29秒前
30秒前
30秒前
冷静幻嫣完成签到,获得积分10
30秒前
32秒前
jiashan发布了新的文献求助10
32秒前
地表最强青铜五完成签到,获得积分20
32秒前
HelloKun发布了新的文献求助10
32秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
Toward a Combinatorial Approach for the Prediction of IgG Half-Life and Clearance 500
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Picture Books with Same-sex Parented Families: Unintentional Censorship 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3969917
求助须知:如何正确求助?哪些是违规求助? 3514626
关于积分的说明 11175060
捐赠科研通 3249928
什么是DOI,文献DOI怎么找? 1795165
邀请新用户注册赠送积分活动 875617
科研通“疑难数据库(出版商)”最低求助积分说明 804891