找回密码
 To register

QQ登录

只需一步,快速开始

扫一扫,访问微社区

Titlebook: Deep Generative Models; Second MICCAI Worksh Anirban Mukhopadhyay,Ilkay Oksuz,Yixuan Yuan Conference proceedings 2022 The Editor(s) (if app

[复制链接]
查看: 16928|回复: 49
发表于 2025-3-21 16:51:52 | 显示全部楼层 |阅读模式
书目名称Deep Generative Models
副标题Second MICCAI Worksh
编辑Anirban Mukhopadhyay,Ilkay Oksuz,Yixuan Yuan
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Deep Generative Models; Second MICCAI Worksh Anirban Mukhopadhyay,Ilkay Oksuz,Yixuan Yuan Conference proceedings 2022 The Editor(s) (if app
描述This book constitutes the refereed proceedings of the Second MICCAI Workshop on Deep Generative Models, DG4MICCAI 2022, held in conjunction with MICCAI 2022, in September 2022. The workshops took place in Singapore. .DG4MICCAI 2022 accepted 12 papers from the 15 submissions received. The workshop focusses on recent algorithmic developments, new results, and promising future directions in Deep Generative Models. Deep generative models such as Generative Adversarial Network (GAN) and Variational Auto-Encoder (VAE) are currently receiving widespread attention from not only the computer vision and machine learning communities, but also in the MIC and CAI community..
出版日期Conference proceedings 2022
关键词artificial intelligence; bioinformatics; color image processing; color images; computer vision; digital i
版次1
doihttps://doi.org/10.1007/978-3-031-18576-2
isbn_softcover978-3-031-18575-5
isbn_ebook978-3-031-18576-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

书目名称Deep Generative Models影响因子(影响力)




书目名称Deep Generative Models影响因子(影响力)学科排名




书目名称Deep Generative Models网络公开度




书目名称Deep Generative Models网络公开度学科排名




书目名称Deep Generative Models被引频次




书目名称Deep Generative Models被引频次学科排名




书目名称Deep Generative Models年度引用




书目名称Deep Generative Models年度引用学科排名




书目名称Deep Generative Models读者反馈




书目名称Deep Generative Models读者反馈学科排名




单选投票, 共有 0 人参与投票
 

0票 0%

Perfect with Aesthetics

 

0票 0%

Better Implies Difficulty

 

0票 0%

Good and Satisfactory

 

0票 0%

Adverse Performance

 

0票 0%

Disdainful Garbage

您所在的用户组没有投票权限
发表于 2025-3-21 21:37:11 | 显示全部楼层
Deep Generative Models978-3-031-18576-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
发表于 2025-3-22 01:38:28 | 显示全部楼层
发表于 2025-3-22 04:51:37 | 显示全部楼层
发表于 2025-3-22 12:09:37 | 显示全部楼层
Abstract Factory (Abstract Factory),s clinical applications. Deep learning-based super-resolution methods provided promising results for improving the spatial resolution of MRSI, but the super-resolved images are often blurry compared to the experimentally-acquired high-resolution images. Attempts have been made with the generative ad
发表于 2025-3-22 13:35:19 | 显示全部楼层
发表于 2025-3-22 18:51:36 | 显示全部楼层
Wilian Gatti Jr,Beaumie Kim,Lynde Tanage analysis. The latent spaces of these models often show semantically meaningful directions corresponding to human-interpretable image transformations. However, until now, their exploration for medical images has been limited due to the requirement of supervised data. Several methods for unsupervi
发表于 2025-3-22 21:15:59 | 显示全部楼层
Wilian Gatti Jr,Beaumie Kim,Lynde Tane problem of inferring pixel-level predictions of brain lesions by only using image-level labels for training. By leveraging recent advances in generative diffusion probabilistic models (DPM), we synthesize counterfactuals of “How would a patient appear if . pathology was not present?”. The differen
发表于 2025-3-23 02:47:50 | 显示全部楼层
发表于 2025-3-23 08:31:21 | 显示全部楼层
Requirements and Specificationslete, or have inconsistencies between observations. Thus, we propose a generative model that not only produces continuous trajectories of fully synthetic patient images, but also imputes missing data in existing trajectories, by estimating realistic progression over time. Our generative model is tra
 关于派博传思  派博传思旗下网站  友情链接
派博传思介绍 公司地理位置 论文服务流程 影响因子官网 SITEMAP 大讲堂 北京大学 Oxford Uni. Harvard Uni.
发展历史沿革 期刊点评 投稿经验总结 SCIENCEGARD IMPACTFACTOR 派博系数 清华大学 Yale Uni. Stanford Uni.
|Archiver|手机版|小黑屋| 派博传思国际 ( 京公网安备110108008328) GMT+8, 2025-5-8 00:31
Copyright © 2001-2015 派博传思   京公网安备110108008328 版权所有 All rights reserved
快速回复 返回顶部 返回列表