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用语言引导的多式域概括用于头癌的结果预测

Rongfang Wang¹, JiaSheng Chen¹, Xinlong Zhang²

¹School of Artificial Intelligence, Xidian University, China.

Computers in biology and medicine

|August 31, 2025

概括

这项研究引入了一种新的语言引导多模式域泛化 (LGMDG) 方法,用于改善不同医院的头癌复发预测. 通过整合临床数据和成像特征,LGMDG增强了模型的概括性,实现了卓越的准确性.

科学领域:

医学成像分析
在瘤学中使用人工智能
域的一般化

背景情况:

由于医疗成像数据在各个机构之间发生变化,因此难以准确预测头癌的复发情况.
现有的领域概括方法往往忽视了结构化的临床信息,这对于跨机构的一致性至关重要.

研究的目的:

提出一个端到端的语言引导多模式域泛化 (LGMDG) 方法来改善头癌复发的预测.
利用成像和临床数据在不同医疗机构中增强模型通用化.

主要方法:

开发了一个基于语言的临床提示的临床域不变特征提取模块.
实施了对比和反面增强的三分支多模式分类网络.
使用多模式分因双线聚合来预测结果,结合领域对抗性学习和交叉模式对齐.

主要成果:

在6个癌症中心使用离开一个域的验证,平均准确率为81. 04%和平均AUC为76. 91%.
与具有可比模型复杂性的11种竞争方法相比,证明了优越的概括性能 (24.83M参数,17.36 GFLOPs).

结论:

通过整合多式数据,拟议的 LGMDG 方法有效地解决了头癌复发预测的领域转移问题.
通过公开可用的代码和模型,LGMDG为开发临床瘤学中强大且可通用的AI模型提供了一个有前途的方法.

关键词:

临床提示进行对比学习域名通用化头和部癌症多模式融合

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