Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis
Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis
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DOI：
10.1073/pnas.1919012117
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发表时间：
2020-06-09
影响因子：
11.1
通讯作者：
Ferrante, Enzo
中科院分区：
文献类型：
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作者：
Larrazabal, Agostina J.;Nieto, Nicolas;Ferrante, Enzo
用于计算机辅助诊断和基于图像的筛查的人工智能（AI）系统正在全球范围内被医疗机构采用。在这样的背景下，生成公平和无偏见的分类器变得至关重要。医学图像计算的研究团体正在努力开发更精确的算法，以帮助医生完成疾病诊断的困难任务。然而，很少有人关注数据库的收集方式以及这可能如何影响人工智能系统的性能。我们的研究揭示了性别平衡在用于训练人工智能系统进行计算机辅助诊断的医学成像数据集中的重要性。我们提供了一项大规模研究支持的经验证据，该研究基于三种深度神经网络架构和两种众所周知的公开可用的X射线图像数据集，用于诊断不同性别失衡条件下的各种胸部疾病。我们发现，当没有实现最低限度的平衡时，代表性不足的性别的表现会持续下降。这给负责管理和批准计算机辅助诊断系统的国家机构敲响了警钟，这些系统应包括明确的性别平衡和多样性建议。我们还建立了一个开放的问题，学术医学图像计算社区，需要解决的新算法赋予的鲁棒性，性别不平衡。
Artificial intelligence (AI) systems for computer-aided diagnosis and image-based screening are being adopted worldwide by medical institutions. In such a context, generating fair and unbiased classifiers becomes of paramount importance. The research community of medical image computing is making great efforts in developing more accurate algorithms to assist medical doctors in the difficult task of disease diagnosis. However, little attention is paid to the way databases are collected and how this may influence the performance of AI systems. Our study sheds light on the importance of gender balance in medical imaging datasets used to train AI systems for computer-assisted diagnosis. We provide empirical evidence supported by a large-scale study, based on three deep neural network architectures and two well-known publicly available X-ray image datasets used to diagnose various thoracic diseases under different gender imbalance conditions. We found a consistent decrease in performance for underrepresented genders when a minimum balance is not fulfilled. This raises the alarm for national agencies in charge of regulating and approving computer-assisted diagnosis systems, which should include explicit gender balance and diversity recommendations. We also establish an open problem for the academic medical image computing community which needs to be addressed by novel algorithms endowed with robustness to gender imbalance.