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一个新的时间衰变放射学综合网络 (TRINet) 用于乳腺癌风险预测

Hong Hui Yeoh¹, Fredrik Strand², Raphaël Phan³

¹Department of Electrical and Robotics Engineering, School of Engineering, Monash University Malaysia, Bandar Sunway 47500, Malaysia.

Medical image analysis

|October 11, 2025

概括

这项研究介绍了TRINet,这是一种用于预测乳腺癌风险的深度学习模型. TRINet使用时间注意力和放射性特征来提供更准确,个性化的乳房学查建议.

科学领域:

人工智能的人工智能
医疗成像医学成像
在瘤学瘤学.

背景情况:

早期发现乳腺癌对于有效治疗至关重要.
目前的风险预测模型缺乏个性化查间隔和时间相关性.
开发适应性风险预测方案对于优化乳房影像查至关重要.

研究的目的:

提出TRINet,一个用于乳腺癌风险预测的新型深度学习架构.
通过整合放射性特征和时间注意力来提高风险估计.
开发一个动态的,多年风险预测工具,用于个性化查.

主要方法:

实施了深度学习架构 (TRINet),关注时间衰减.
集成的放射性特征与基于注意力的多实例学习 (AMIL) 框架.
利用双边不对称的持续学习和时间嵌入的附加危险层.

主要成果:

在EMBED数据集上,TRINet的性能与最先进的模型相美.
在风险预测方面,AUC得分从0.851 (1年) 到0.789 (5年) 不等.
证明了时间注意力,放射性特征和持续学习的有效性.

结论:

TRINet为预测乳腺癌风险提供了一个更具适应性和精确性的工具.
时间动态和不对称性的整合改善了风险预测.
这种方法支持个性化的乳房学查方案.

关键词:

癌症风险预测癌症风险预测计算机辅助诊断是一种计算机辅助的诊断.乳房学乳房学乳房学无线电学 (Radiomics) 是一种放射学.

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