面向少数民族医学分割训练集的无监督搜索
Unsupervised Search for Ethnic Minorities' Medical Segmentation Training Set
AI总结：
针对医学分割数据集中的种族代表性偏差，本文提出无监督贪心搜索策略，从现有数据中筛选匹配少数族裔目标域分布的训练图像，提升了对黑人群体的分割性能。
摘要：
本文研究医学成像中数据集偏差这一关键问题，特别强调数据集采集过程中因人群分布不均所导致的种族差异。我们的分析表明，医学分割数据集存在显著偏差，这主要受其采集地点人口构成的影响。例如，在美国采集的扫描激光检眼镜（Scanning Laser Ophthalmoscopy，SLO）眼底数据集主要以白人个体的图像为主，少数族裔群体的代表性不足。这种不平衡可能导致模型性能出现偏差，并造成不公平的临床结果，尤其会影响少数族裔人群。为应对这一挑战，我们提出了一种新颖的训练集搜索策略，旨在通过关注代表性不足的种族群体来减少这些偏差。我们的方法利用现有数据集，并采用一种简单的贪心算法来识别与目标域分布高度匹配的源图像。通过选择与少数族裔人群特征更一致的训练数据，该策略提高了医学分割模型在特定少数族裔（即黑人）上的准确率。我们的实验结果证明了该方法在缓解偏差方面的有效性。我们还讨论了更广泛的社会影响，强调解决这些差异如何有助于实现更公平的医疗结果。
英文摘要：
This article investigates the critical issue of dataset bias in medical imaging, with a particular emphasis on racial disparities caused by uneven population distribution in dataset collection. Our analysis reveals that medical segmentation datasets are significantly biased, primarily influenced by the demographic composition of their collection sites. For instance, Scanning Laser Ophthalmoscopy (SLO) fundus datasets collected in the United States predominantly feature images of White individuals, with minority racial groups underrepresented. This imbalance can result in biased model performance and inequitable clinical outcomes, particularly for minority populations. To address this challenge, we propose a novel training set search strategy aimed at reducing these biases by focusing on underrepresented racial groups. Our approach utilizes existing datasets and employs a simple greedy algorithm to identify source images that closely match the target domain distribution. By selecting training data that aligns more closely with the characteristics of minority populations, our strategy improves the accuracy of medical segmentation models on specific minorities, i.e., Black. Our experimental results demonstrate the effectiveness of this approach in mitigating bias. We also discuss the broader societal implications, highlighting how addressing these disparities can contribute to more equitable healthcare outcomes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
