Importance of phase enhancement for machine learning classification of solid renal masses using texture analysis features at multi-phasic CT
Author:
Publisher
Springer Science and Business Media LLC
Subject
Urology,Gastroenterology,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology
Link
http://link.springer.com/content/pdf/10.1007/s00261-020-02632-1.pdf
Reference49 articles.
1. Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ. CT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges. RadioGraphics. 2017;37(5):1483-503.
2. Vendrami CL, Velichko YS, Miller FH, et al. Differentiation of Papillary Renal Cell Carcinoma Subtypes on MRI: Qualitative and Texture Analysis. American Journal of Roentgenology. 2018;211(6):1234-45.
3. Raman SP, Chen Y, Schroeder JL, Huang P, Fishman EK. CT Texture Analysis of Renal Masses: Pilot Study Using Random Forest Classification for Prediction of Pathology. Academic Radiology. 2014;21(12):1587-96.
4. Hodgdon T, McInnes MDF, Schieda N, Flood TA, Lamb L, Thornhill RE. Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images? Radiology. 2015;276(3):787-96.
5. Feng Z, Rong P, Cao P, et al. Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma. European Radiology. 2018;28(4):1625-33.
Cited by 11 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. Enhanced deep learning based decision support system for kidney tumour detection;BenchCouncil Transactions on Benchmarks, Standards and Evaluations;2024-06
2. Automated abdominal CT contrast phase detection using an interpretable and open-source artificial intelligence algorithm;European Radiology;2024-04-29
3. Improved Classification of Kidney Lesions in CT Scans Using CNN with Attention Layers: Achieving High Accuracy and Performance;Studies in Big Data;2024
4. A Deep Learning Framework for Kidney Stone Prediction;Lecture Notes in Electrical Engineering;2024
5. Differentiate Xp11.2 Translocation Renal Cell Carcinoma from Computed Tomography Images and Clinical Data with ResNet-18 CNN and XGBoost;Computer Modeling in Engineering & Sciences;2023
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