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Updated: Jun 27, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

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一种基于临床,成像,结肠镜和病理学特征的新型多学科机器学习方法,用于区分肠结核病和克罗恩病的肠道结核病

Baolan Lu1, Zengan Huang2, Jinjiang Lin1

  • 1Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan II Road, Guangzhou, 510080, Guangdong, People's Republic of China.

Abdominal radiology (New York)
|May 4, 2024
PubMed
概括

一个新的多学科模型使用机器学习有效地区分了肠结核病 (ITB) 和克罗恩病 (CD). 这种方法整合了临床,MRI,结肠镜和病理学数据,以提高诊断准确度.

关键词:
克罗恩氏病是什么 克罗恩氏病是什么肠道结核病 肠道结核病机器学习是机器学习.磁共振肠道学 磁共振肠道学

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model07:15

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