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基于乳腺DCE-MRI衍生放射学特征的随机森林模型中的种族偏见的调查

Mohamed Huti¹, Tiarna Lee¹, Elinor Sawyer²

¹School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.

Clinical image-based procedures, fairness of AI in medical imaging, and ethical and philosophical issues in medical imaging : 12th International Workshop, CLIP 2023 1st International Workshop, FAIMI 2023 and 2nd International Workshop, ...

|October 15, 2024

概括

人工智能 (AI) 模型可以显示种族偏见. 在乳腺癌成像数据上训练的随机森林模型可以预测种族,并且在训练的种族数据上表现更好.

科学领域:

医疗成像医学成像
人工智能的人工智能
机器学习机器学习

背景情况:

人工智能 (AI) 模型可以在不平衡的数据集上训练时表现出性能偏差.
以前的研究主要集中在深度学习模型上,忽视了使用手工制作的功能在经典AI技术中的潜在偏见.

研究的目的:

为了调查随机森林 (RF) 模型中的种族偏见,这些模型使用了放射学特征进行训练.
评估从DCE-MRI数据中预测乳腺癌分子亚型的潜在偏差.

主要方法:

利用了从乳腺癌患者的动态对比增强磁共振成像 (DCE-MRI) 中提取的放射性特征.
训练随机森林 (RF) 模型,从放射性特征来预测种族 (白色和黑色).
训练有素的射频模型使用种族不平衡的DCE-MRI数据来预测瘤分子亚型.

主要成果:

来自DCE-MRI的放射特征包含可以识别种族的信息.
射频模型在预测白人和黑人种族方面达到60-70%的准确性.
在种族不平衡数据上训练的射频模型表现出有偏见的表现,有利于训练集中所代表的种族.

结论:

像RF这样的经典AI模型在使用放射学特征时可能容易受到种族偏见的影响.
医学AI中的种族不平衡数据可能导致瘤分子亚型的偏见预测模型.
需要进一步的研究来减轻医疗应用的AI模型中的偏差.

关键词:

在这里,我们可以看到AIAIAI.这是一个偏见的偏见.乳房乳房乳房在DCE-MRI中,使用的是DCE-MRI.公平的公平的公平的无线电学 (Radiomics) 是一种无线电学.

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