Robotics & Machine Learning Daily News2024,Issue(Dec.5) :109-109.

Researchers’ Work from Affiliated Hospital of Qingdao University Focuses on Supp ort Vector Machines (Different radiomics models in predicting the malignant pote ntial of small intestinal stromal tumors)

青岛大学附属医院研究人员的工作重点是支持向量机(不同的放射组学模型预测小肠间质瘤恶性病变)

Robotics & Machine Learning Daily News2024,Issue(Dec.5) :109-109.

Researchers’ Work from Affiliated Hospital of Qingdao University Focuses on Supp ort Vector Machines (Different radiomics models in predicting the malignant pote ntial of small intestinal stromal tumors)

青岛大学附属医院研究人员的工作重点是支持向量机(不同的放射组学模型预测小肠间质瘤恶性病变)

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摘要

由一名新闻记者-机器人与机器学习日报的工作人员新闻编辑每日新闻-支持向量机的新数据在一份新报告中呈现。根据来自中国山东的新闻报道,由NewsRx编辑,研究表明,“To”不同放射组学模型预测小肠恶性潜能的可行性探讨间质瘤(SISTs),并选择最佳的放射组学模型。140例临床分析与SISS进行了合作。放射组学特征是从CT增强的图像中提取的。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Fresh data on support vector machines are presented in a new report. According tonews reporting out of Shandong, Peop le’s Republic of China, by NewsRx editors, research stated, “Toexplore the feas ibility of different radiomics models for predicting the malignant potential of small intestinalstromal tumors (SISTs), and to select the best radiomics model. A retrospective analysis of 140 patientswith SISTs was conducted. Radiomics fe atures were extracted from CT-enhanced images.”

Key words

Affiliated Hospital of Qingdao Universit y/Shandong/People’s Republic of China/Asia/Machine Learning/Support Vector Machines

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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