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Updated: Jul 18, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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探索视觉转换器和XGBoost作为深度学习组合,用于转换癌瘤识别

Akella Subrahmanya Narasimha Raju1, K Venkatesh2, B Padmaja3

  • 1Department of Computer Science and Engineering (Data Science), Institute of Aeronautical Engineering, Dundigul, Hyderabad, Telangana, 500043, India. a.raju@iare.ac.in.

Scientific reports
|December 3, 2024
PubMed
概括

这项研究引入了用于早期结直肠癌 (CRC) 检测的新型深度学习组合. 这种先进的方法显著提高了使用联合CNN模型和视觉转换器或XGBoost的诊断准确性.

关键词:
在CKHK-22数据集中.结肠直肠癌 (CRC) 是一种癌症.组合模型组合模型集成的CNNs可以实现.视觉转换器 视觉转换器在XGBoost中使用.

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