不可见属性，可见偏见：探索基于MRI的阿尔茨海默病分类中的人口统计学捷径
Invisible Attributes, Visible Biases: Exploring Demographic Shortcuts in MRI-based Alzheimer's Disease Classification
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中文总结 AI 辅助
本研究探讨基于MRI的阿尔茨海默病分类中深度学习模型通过种族和性别捷径学习导致的性能偏见，使用多个数据集和模型验证，为公平诊断工具奠定基础。
中文摘要 AI 辅助
磁共振成像（MRI）是脑成像的金标准。深度学习（DL）算法已被提出用于辅助从MRI扫描中诊断阿尔茨海默病（AD）等疾病。然而，DL算法可能遭受捷径学习的影响，即使用与输出标签不直接相关的虚假特征进行预测。当这些特征与受保护属性相关时，可能导致对代表性不足的受保护群体（如按种族和性别定义的群体）的性能偏见。在本工作中，我们探讨了基于MRI的DL AD诊断中捷径学习和人口统计学偏见的可能性。我们首先调查DL算法是否能够从3D脑MRI扫描中识别种族或性别，以确定是否存在基于种族和性别的分布偏移。接下来，我们研究训练集在种族或性别上的不平衡是否会导致模型性能下降，从而表明捷径学习和偏见的存在。最后，我们对受保护属性和AD分类任务中不同脑区的特征归因进行了定量和定性分析。通过这些实验，并使用多个数据集和DL模型（ResNet和SwinTransformer），我们证明了在基于DL的AD分类中存在基于种族和性别的捷径学习和偏见。我们的工作为脑MRI中更公平的DL诊断工具奠定了基础。代码可在https://github.com/acharaakshit/ShortMR获取。
英文摘要
Magnetic resonance imaging (MRI) is the gold standard for brain imaging. Deep learning (DL) algorithms have been proposed to aid in the diagnosis of diseases such as Alzheimer's disease (AD) from MRI scans. However, DL algorithms can suffer from shortcut learning, in which spurious features, not directly related to the output label, are used for prediction. When these features are related to protected attributes, they can lead to performance bias against underrepresented protected groups, such as those defined by race and sex. In this work, we explore the potential for shortcut learning and demographic bias in DL based AD diagnosis from MRI. We first investigate if DL algorithms can identify race or sex from 3D brain MRI scans to establish the presence or otherwise of race and sex based distributional shifts. Next, we investigate whether training set imbalance by race or sex can cause a drop in model performance, indicating shortcut learning and bias. Finally, we conduct a quantitative and qualitative analysis of feature attributions in different brain regions for both the protected attribute and AD classification tasks. Through these experiments, and using multiple datasets and DL models (ResNet and SwinTransformer), we demonstrate the existence of both race and sex based shortcut learning and bias in DL based AD classification. Our work lays the foundation for fairer DL diagnostic tools in brain MRI. The code is provided at https://github.com/acharaakshit/ShortMR
发表机构
- School of Biomedical Engineering（生物医学工程学院）
- Imaging Sciences, King's College London, UK（影像科学，伦敦国王学院，英国）
机构由 AI 辅助整理，请以论文原文为准。
