Using Autoencoders to Reduce Dimensionality of DICOM Metadata
Author:
Affiliation:
1. University of Rijeka,Faculty of Engineering,Rijeka,Croatia,51000
2. University of Rijeka, Clinical Hospital Centre Rijeka,Rijeka,Croatia,51000
3. Medical University of Graz,Graz,Austria,8036
Funder
Croatian Science Foundation
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9987724/9987729/09988310.pdf?arnumber=9988310
Reference21 articles.
1. Using DICOM Tags for Clustering Medical Radiology Images into Visually Similar Groups
2. Human embryonic stem cell classification: random network with autoencoded feature extractor
3. Predicting the fidelity of JPEG2000 compressed CT images using DICOM header information
4. Quality of DICOM header information for image catego-rization;guld;Proceedings of SPIE - The International Society for Optical Engineering,2002
5. Deep Learning using Rectified Linear Units (ReLU);agarap;CoRR vol abs/1803 08375,2018
Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Building RadiologyNET: an unsupervised approach to annotating a large-scale multimodal medical database;BioData Mining;2024-07-12
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3