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对于在组织病理学-A结肠癌使用案例中未知偏差的因果失调

Ramón L Correa-Medero^1,2, Rish Pai³, Kingsley Ebare³

¹School of Computing And Augmented Intelligence Arizona State University, Phoenix, Arziona, United States of America.

|November 22, 2024

概括

本研究引入了一种因果生存模型,以减少人工智能 (AI) 基因病理学模型中的偏差. 该模型提高了AI在不同地点的概括性和性能,用于预测结直肠癌存活率.

科学领域:

数字病理学数字病理学
医学中的人工智能.
因果推理的原因推理.

背景情况:

数字组织病理学中的AI提供了准确的诊断和预后.
在外部数据集上,人工智能模型的性能因数据采集差异和采样偏差而下降.
人工智能模型中的偏差可以导致不同人群的不平等表现,需要在临床使用之前检测和删除偏差.

研究的目的:

提出一种因果生存模型,以减轻人工智能的未知偏差.
提高AI模型在不同地理位置的概括性和性能均等.
用定量组织病理学特征预测结直肠癌患者的无复发生存率.

主要方法:

利用因果推理框架来开发因果生存模型.
利用来自七个地理分布的地点的定量组织病理学特征,用于结直肠癌患者数据.
将拟议模型与传统的考克斯比例危险和深度生存模型进行比较.

主要成果:

与基线模型相比,因果生存模型在各个地点实现了均等的性能.
废弃研究表明了潜伏概率调整和辅助损失的好处.
该模型有效地减少了AI预测中未知偏差的影响.

结论:

提出的因果失调解决方案提高了AI模型在组织病理学中的概括性.
因果生存模型为预后提供了一种标准化和准确的方法.
虽然偏差原因的检测仍然是一个挑战,但这种方法提供了一个实用的 debiasing 解决方案.

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