• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

In the Era of Deep Learning, Why Reconstruct an Image at All?

作者信息

Chung Caroline, Kalpathy-Cramer Jayashree, Knopp Michael V, Jaffray David A

机构信息

Director of Imaging Technology and Innovation, Radiation Oncology, and Co-Chair, Data Governance Program, Co-Chair, MR Coordinating Committee and Dynamic Contrast-Enhanced MRI Committee, and Quantitative Imaging Biomarker Alliance, The University of Texas MD Anderson Cancer Center, Houston, Texas.

Co-Director, The Quantitative Translational Imaging in Medicine Lab and Center for Machine Learning, Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts.

出版信息

J Am Coll Radiol. 2021 Jan;18(1 Pt B):170-173. doi: 10.1016/j.jacr.2020.09.050.

DOI:10.1016/j.jacr.2020.09.050
PMID:33413895
Abstract
摘要

相似文献

1
In the Era of Deep Learning, Why Reconstruct an Image at All?在深度学习时代,为何还要进行图像重建?
J Am Coll Radiol. 2021 Jan;18(1 Pt B):170-173. doi: 10.1016/j.jacr.2020.09.050.
2
Why Radiologists Have Nothing to Fear From Deep Learning.放射科医生为何无需惧怕深度学习
J Am Coll Radiol. 2019 Sep;16(9 Pt A):1190-1192. doi: 10.1016/j.jacr.2019.02.037. Epub 2019 Apr 18.
3
A gentle introduction to deep learning in medical image processing.深度学习在医学图像处理中的应用简介。
Z Med Phys. 2019 May;29(2):86-101. doi: 10.1016/j.zemedi.2018.12.003. Epub 2019 Jan 25.
4
When Deep Learning Meets Cell Image Synthesis.
Cytometry A. 2020 Mar;97(3):222-225. doi: 10.1002/cyto.a.23957. Epub 2019 Dec 30.
5
Deep Learning in Microscopy Image Analysis: A Survey.深度学习在显微镜图像分析中的应用:综述。
IEEE Trans Neural Netw Learn Syst. 2018 Oct;29(10):4550-4568. doi: 10.1109/TNNLS.2017.2766168. Epub 2017 Nov 22.
6
Improvement of image quality at CT and MRI using deep learning.利用深度学习提高CT和MRI图像质量。
Jpn J Radiol. 2019 Jan;37(1):73-80. doi: 10.1007/s11604-018-0796-2. Epub 2018 Nov 29.
7
Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction.深度学习在荧光图像重建中的应用、前景与挑战。
Nat Methods. 2019 Dec;16(12):1215-1225. doi: 10.1038/s41592-019-0458-z. Epub 2019 Jul 8.
8
Hello World Deep Learning in Medical Imaging.你好,医学影像深度学习。
J Digit Imaging. 2018 Jun;31(3):283-289. doi: 10.1007/s10278-018-0079-6.
9
Quantitative Imaging of Body Fat Distribution in the Era of Deep Learning.深度学习时代的体脂分布定量成像
Acad Radiol. 2021 Nov;28(11):1488-1490. doi: 10.1016/j.acra.2021.04.004. Epub 2021 May 19.
10
The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis.图像预处理和后处理技术对深度学习框架的影响:数字病理学图像分析的全面综述
Comput Biol Med. 2021 Jan;128:104129. doi: 10.1016/j.compbiomed.2020.104129. Epub 2020 Nov 21.

引用本文的文献

1
Conference Report: Review of Clinical Implementation of Advanced Quantitative Imaging Techniques for Personalized Radiotherapy.会议报告:高级定量成像技术在个性化放疗临床应用中的回顾。
Tomography. 2024 Nov 14;10(11):1798-1813. doi: 10.3390/tomography10110132.
2
Evolving and Novel Applications of Artificial Intelligence in Thoracic Imaging.人工智能在胸部成像中的不断发展与新应用
Diagnostics (Basel). 2024 Jul 8;14(13):1456. doi: 10.3390/diagnostics14131456.
3
Reproducibility in Radiomics: A Comparison of Feature Extraction Methods and Two Independent Datasets.
放射组学中的可重复性:特征提取方法与两个独立数据集的比较
Appl Sci (Basel). 2024 Feb 20;166(1). doi: 10.3390/app13127291.
4
Artificial Intelligence in CT and MR Imaging for Oncological Applications.用于肿瘤学应用的CT和MR成像中的人工智能
Cancers (Basel). 2023 Apr 30;15(9):2573. doi: 10.3390/cancers15092573.
5
Cancer Needs a Robust "Metadata Supply Chain" to Realize the Promise of Artificial Intelligence.癌症需要一个强大的“元数据供应链”来实现人工智能的承诺。
Cancer Res. 2021 Dec 1;81(23):5810-5812. doi: 10.1158/0008-5472.CAN-21-1929.
6
A maChine and deep Learning Approach to predict pulmoNary hyperteNsIon in newbornS with congenital diaphragmatic Hernia (CLANNISH): Protocol for a retrospective study.一种机器学习和深度学习方法预测先天性膈疝新生儿肺高压(CLANNISH):一项回顾性研究方案。
PLoS One. 2021 Nov 9;16(11):e0259724. doi: 10.1371/journal.pone.0259724. eCollection 2021.
7
Making Radiomics More Reproducible across Scanner and Imaging Protocol Variations: A Review of Harmonization Methods.提高放射组学在不同扫描仪和成像协议之间的可重复性:协调方法综述。
J Pers Med. 2021 Aug 27;11(9):842. doi: 10.3390/jpm11090842.