多模式多实例证据融合神经网络用于癌症生存率预测
Hui Luo1,2, Jiashuang Huang3, Hengrong Ju3
1Faculty of Data Science, City University of Macau, Macau, 999078, China.
Scientific reports
|March 27, 2025
概括
这项研究引入了一种新的AI模型,用于使用多式联络数据预测癌症生存率. 该模型通过考虑全球背景和量化数据不确定性来提高准确性,以便更可靠的临床决策.
科学领域:
- 在瘤学瘤学.
- 人工智能的人工智能
- 生物医学信息学 生物医学信息学
背景情况:
- 准确的癌症生存预测对于治疗计划至关重要.
- 多模式数据 (组织病理学,基因组学,临床) 提高了预测的准确性.
- 现有的方法与全球背景和模式不确定性作斗争,影响可靠性.
研究的目的:
- 开发一种新的多式联络神经网络,用于增强癌症存活率预测.
- 解决利用全球背景和处理模式不确定性的局限性.
主要方法:
- 提出了一个多模式的多实例证据融合神经网络 (M2EF-NNs).
- 使用预先训练的视觉变压器用于基因病理图像全球背景.
- 应用Dempster-Shafer证据理论和主观逻辑来估计模式不确定性.
- 动态调整的融合数据权重可用于可信的预测.
主要成果:
- 在三个数据集上显著提高了癌症存活率预测准确度.
- 在整体C指数和AUC方面表现有所提高.
- 验证了模型在生存预测中的可靠性.
结论:
- M2EF-NN模型提供了一种可靠的方法来预测癌症存活率.
- 有效地整合全球背景和模式的不确定性,以改善临床决策支持.
- 推进人工智能在个性化瘤学的应用.
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