National and Kapodistrian University of Athens Reports Findings in Machine Learn ing (Accuracy of distinguishing benign, high-risk lesions and malignancies with inductive machine learning models in BIRADS 4 and BIRADS 5 lesions on breast MR …)

雅典国立和卡波迪兰大学报告调查结果机器学习（鉴别良性、高风险的准确性疾病和恶性肿瘤的归纳机器学习模型乳腺MR上的BIRADS 4和BIRADS 5病灶。

Robotics & Machine Learning Daily News2024，Issue(Nov.21) ：41-41.

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National and Kapodistrian University of Athens Reports Findings in Machine Learn ing (Accuracy of distinguishing benign, high-risk lesions and malignancies with inductive machine learning models in BIRADS 4 and BIRADS 5 lesions on breast MR …)

雅典国立和卡波迪兰大学报告调查结果机器学习（鉴别良性、高风险的准确性疾病和恶性肿瘤的归纳机器学习模型乳腺MR上的BIRADS 4和BIRADS 5病灶。

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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-机器学习的新研究是一篇报道的主题。据新闻报道NewsR X记者从希腊雅典报道，研究称：“这项研究的目的是探讨归纳决策树模型(IDTs)在区分良性、马里纳特高风险（B3）乳腺病变。我们分析了114例患者中124个经组织学证实的ed病灶。使用BI-RADS 4或5名评估人员进行乳腺MR检查。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Machine Learning is th e subject of a report. According to newsreporting from Athens, Greece, by NewsR x journalists, research stated, “The aim of this study is toexplore the utility of Inductive Decision Tree models (IDTs) in distinguishing between benign, mali gnant,and high-risk (B3) breast lesions. We analyzed 124 histologically confirm ed lesions in 114 patients whounderwent breast MR with BI-RADS 4 or 5 assessmen t.”

Key words

Athens/Greece/Europe/Cyborgs/Emergin g Technologies/Machine Learning

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出版年

2024

Robotics & Machine Learning Daily News

ISSN：

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