首页> 外文OA文献 >Noninvasive Fuhrman grading of clear cell renal cell carcinoma using computed tomography radiomic features and machine learning

【2h】

Noninvasive Fuhrman grading of clear cell renal cell carcinoma using computed tomography radiomic features and machine learning

机译：利用计算机断层扫描射线特征和机器学习，透明细胞肾细胞癌的非侵入性Fuhrman分级

代理获取

本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。

页面导航

著录项
引文网络
相似文献
相关主题

著录项

作者
Mostafa Nazari; Isaac Shiri; Ghasem Hajianfar; Niki Oveisi; Hamid Abdollahi; Mohammad Reza Deevband; Mehrdad Oveisi; Habib Zaidi;
展开▼
作者单位

展开▼
年度 2020
总页数
原文格式 PDF
正文语种
中图分类

相似文献

外文文献
中文文献
专利

1. Clear Cell Renal Cell Carcinoma: Machine Learning-Based Quantitative Computed Tomography Texture Analysis for Prediction of Fuhrman Nuclear Grade [J] . Bektas Ceyda Turan, Kocak Burak, Yardimci Aytul Hande, European radiology . 2019,第3期

机译：透明细胞肾细胞癌：基于机器学习的定量计算断层扫描纹理分析，以预测Fuhrman核级
2. Grading of Clear Cell Renal Cell Carcinomas by Using Machine Learning Based on Artificial Neural Networks and Radiomic Signatures Extracted From Multidetector Computed Tomography Images [J] . He Xiaopeng, Wei Yi, Zhang Hanmei, Mathematical research letters: MRL . 2020,第2期

机译：基于人工神经网络的机器学习，用机器学习和从多校制计算断层摄影图像提取的机器学习进行透明细胞肾细胞癌的分级
3. Grading of Clear Cell Renal Cell Carcinomas by Using Machine Learning Based on Artificial Neural Networks and Radiomic Signatures Extracted From Multidetector Computed Tomography Images [J] . He Xiaopeng, Wei Yi, Zhang Hanmei, Academic radiology . 2020,第2期

机译：基于人工神经网络的机器学习，用机器学习和从多校制计算断层摄影图像提取的机器学习进行透明细胞肾细胞癌的分级
4. An Effective Radiomics Model for Noninvasive Discrimination of Fat-poor Angiomyolipoma from Clear Cell Renal Cell Carcinoma [C] . Yi Yang, Jianbing Zhu, Zhiyong Zhou, IEEE Symposium Series on Computational Intelligence . 2019

机译：从透明细胞肾细胞癌无脂肪鉴别血管性脂肪异常的有效放射学模型。
5. Characterization of Computed Tomography Radiomic Features using Texture Phantoms [D] . Hassan, Muhammad Shafiq ul 2018

机译：使用纹理幻影表征计算机断层摄影放射学特征
6. Preoperative Predicting the WHO/ISUP Nuclear Grade of Clear Cell Renal Cell Carcinoma by Computed Tomography-Based Radiomics Features [O] . Claudia-Gabriela Moldovanu, Bianca Boca, Andrei Lebovici, 2021

机译：术前预测通过基于计算机断层扫描的辐射瘤特征预测透明细胞肾细胞癌的世卫组织/ isup核等级
7. Comparison of Different Machine Models Based on Contrast-Enhanced Computed Tomography Radiomic Features to Differentiate High From Low Grade Clear Cell Renal Cell Carcinomas [O] . Xu Pei, Ping Wang, Jia-Liang Ren, 2021

机译：基于对比度增强的计算断层扫描射域特征的不同机器模型的比较，从低等级透明细胞肾细胞癌区分高

Noninvasive Fuhrman grading of clear cell renal cell carcinoma using computed tomography radiomic features and machine learning

著录项

引文网络

相似文献

相关主题

期刊订阅