SCH: Quantifying and mitigating demographic biases of machine learning in real world radiology

项目总结(见说明)： 现代机器学习算法在放射学中的应用持续增长，因为这些工具 代表在诊断和诊断的效率、可获得性和准确性方面的潜在巨大改进 筛查工具。与此同时，这些日益复杂的机器学习模型可能存在偏见 对代表不足的人口群体的个人的预测，可能会永久化 先前存在的健康差距。这种对公平的担忧在公共卫生中尤为重要 专注于大规模基于人群的筛查的应用，如乳腺癌和癌症筛查 肺癌。在这些环境中，了解机器学习筛选的频率是至关重要的 算法可能是不公平和有偏见的，以及如何缩小这些差异。这项提议将得到发展。 工具用于量化、校正和分析预测算法与不同 真实世界环境中的人口统计群体。特别是，我们将开发分析和算法来 量化机器学习模型在以下情况下违反公平的情况： 敏感属性本身(如生物性别、种族或年龄)不能直接观察到，我们会 提供纠正最坏情况下违反公平的算法。我们将在下面分析我们的工具 分布转移，即存在人群差异，这在大规模癌症筛查中很常见 程序。该项目还将对最高级别的训练样本和特征进行推理 与违反公平行为相关联，从而为制定解决方案提供指导，以防止 有偏见的算法在未来。我们的工具将在各种大型真实放射学上进行验证 涵盖多种成像方式的数据集，包括包括肺部在内的常规胸部X光数据集 癌症诊断(CheXpert和MIMIC-CXR)，以及Emory乳腺癌成像数据集 (Emed)和国家肺癌筛查试验，评估和纠正 关于生物性别(在适当情况下)、种族和年龄的预测算法。这样做的结果 该项目将建立关于医学领域机器学习模型倾向的关键知识 影像诊断和癌症筛查是不公平和有偏见的，以及量化的基础工具 并减轻这些潜在改变游戏规则的技术中的这些偏见。

PROJECT SUMMARY (See instructions): The application of modern machine learning algorithms in radiology continues to grow, as these tools represent potential huge improvements in efficiency, accessibility and accuracy of diagnostic and screening tools. At the same time, these increasingly complex machine learning models can have biased predictions against individuals of under-represented demographic groups, potentially perpetuating pre-existing health disparities. Such fairness concerns are particularly important in public health applications that focus on large scale population-based screening, as in cancer screening for breast and lung cancer. In these settings, it is paramount to understand how often machine learning screening algorithms can be unfair and biased, and how to mitigate these disparities. This proposal will develop tools to quantify, correct, and analyze the biases of predictive algorithms in relation to different demographic groups in real world settings. In particular, we will develop analysis and algorithms to quantify the violation of fairness by a machine learning model in situations where information about the sensitive attribute itself (such as biological sex, race or age) are not directly observable, and we will provide algorithms that correct for their worst-case fairness violations. We will analyze our tools under distribution shifts, whereby differences in populations exist, as is common in large scale cancer screening programs. This project will also perform inference on the training samples and features most highly associated with fairness violations, thereby providing guidance on the development of solutions to prevent biased algorithms in the future. Our tools will be validated on a variety of large real-world radiology datasets spanning multiple imaging modalities, including general chest X-ray datasets that include lung cancer diagnoses (CheXpert and MIMIC-CXR), as well as the Emory Breast Cancer Imaging Dataset (EMBED) and the National Lung Cancer Screening Trial, evaluating and correcting disparities for predictive algorithms with respect to biological sex (where appropriate), race, and age. The results of this project will establish critical knowledge about the propensity of machine learning models for medical imaging diagnosis and cancer screening to be unfair and biased, as well as foundational tools to quantify and mitigate these biases in these potentially game-changing technologies.