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Updated: Jun 3, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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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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自主监督学习揭示了临床相关的组织形态学模式,用于结肠癌治疗策略

Bojing Liu1,2, Meaghan Polack3, Nicolas Coudray2,4

  • 1Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Karolinska, Sweden.

Nature communications
|March 8, 2025
PubMed
概括

自主监督学习 (SSL) 在结肠癌整片图像中确定了47个组织形态表型群 (HPC). 这些HPC预测患者的生存率和治疗反应,帮助个性化医疗.

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Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

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

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...