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: Machine Learning in Dentistry; Ching-Chang Ko,Dinggang Shen,Li Wang Book 2021 Springer Nature Switzerland AG 2021 Dental big data.Digital

[复制链接]
查看: 10646|回复: 54
发表于 2025-3-21 16:43:07 | 显示全部楼层 |阅读模式
书目名称Machine Learning in Dentistry
编辑Ching-Chang Ko,Dinggang Shen,Li Wang
视频video
概述Reviews use of machine learning in contemporary dentistry.Covers applications in dental practice and research.Highlights benefits, opportunities, and challenges
图书封面Titlebook: Machine Learning in Dentistry;  Ching-Chang Ko,Dinggang Shen,Li Wang Book 2021 Springer Nature Switzerland AG 2021 Dental big data.Digital
描述This book reviews all aspects of the use of machine learning in contemporary dentistry, clearly explaining its significance for dental imaging, oral diagnosis and treatment, dental designs, and dental research. Machine learning is an emerging field of artificial intelligence research and practice in which computer agents are employed to improve perception, cognition, and action based on their ability to “learn”, for example through use of big data techniques. Its application within dentistry is designed to promote personalized and precision patient care, with enhancement of diagnosis and treatment planning. In this book, readers will find up-to-date information on different machine learning tools and their applicability in various dental specialties. The selected examples amply illustrate the opportunities to employ a machine learning approach within dentistry while also serving to highlight the associated challenges.. .Machine Learning in Dentistry. will be of value for alldental practitioners and researchers who wish to learn more about the potential benefits of using machine learning techniques in their work.
出版日期Book 2021
关键词Dental big data; Digital oral imaging; Digital Dentistry; Oral diagnosis; Artificial intelligence in den
版次1
doihttps://doi.org/10.1007/978-3-030-71881-7
isbn_softcover978-3-030-71883-1
isbn_ebook978-3-030-71881-7
copyrightSpringer Nature Switzerland AG 2021
The information of publication is updating

书目名称Machine Learning in Dentistry影响因子(影响力)




书目名称Machine Learning in Dentistry影响因子(影响力)学科排名




书目名称Machine Learning in Dentistry网络公开度




书目名称Machine Learning in Dentistry网络公开度学科排名




书目名称Machine Learning in Dentistry被引频次




书目名称Machine Learning in Dentistry被引频次学科排名




书目名称Machine Learning in Dentistry年度引用




书目名称Machine Learning in Dentistry年度引用学科排名




书目名称Machine Learning in Dentistry读者反馈




书目名称Machine Learning in Dentistry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 22:00:53 | 显示全部楼层
发表于 2025-3-22 01:23:40 | 显示全部楼层
发表于 2025-3-22 04:48:59 | 显示全部楼层
Hannah H. Deng,Li Wang,Yi Ren,Jaime Gateno,Zhen Tang,Ken-Chung Chen,Chunfeng Lian,Steve Guofang Shen
发表于 2025-3-22 11:34:22 | 显示全部楼层
发表于 2025-3-22 14:11:49 | 显示全部楼层
Shankar Rengasamy Venugopalan,Mohammed H. Elnagar,Deepti S. Karhade,Veerasathpurush Allareddy
发表于 2025-3-22 18:28:20 | 显示全部楼层
Alonso Carrasco-Labra,Olivia Urquhart,Heiko Spallek
发表于 2025-3-22 21:15:47 | 显示全部楼层
Di Wu,Deepti S. Karhade,Malvika Pillai,Min-Zhi Jiang,Le Huang,Gang Li,Hunyong Cho,Jeff Roach,Yun Li,
发表于 2025-3-23 03:04:18 | 显示全部楼层
发表于 2025-3-23 07:26:15 | 显示全部楼层
Machine Learning for CBCT Segmentation of Craniomaxillofacial Bony Structuresf expert-segmented CBCT images as the atlases to perform majority voting for the estimation of the initial segmentation probability maps for an input CBCT image. Guided by the contextual prior provided by the initial probability maps, an auto-context random forest is constructed, which uses the appe
 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, 2025-5-16 19:43
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