通过对比学习提高胸片自动诊断的公平性

IF 8.1 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Radiology-Artificial Intelligence Pub Date : 2024-09-01 DOI:10.1148/ryai.230342
Mingquan Lin, Tianhao Li, Zhaoyi Sun, Gregory Holste, Ying Ding, Fei Wang, George Shih, Yifan Peng
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引用次数: 0

摘要

"刚刚接受 "的论文经过同行评审,已被接受在《放射学》上发表:人工智能》上发表。这篇文章在以最终版本发表之前,还将经过校对、排版和校对审核。请注意,在制作最终校对稿的过程中,可能会发现影响内容的错误。目的 开发一种人工智能模型,利用有监督的对比学习最大程度地减少胸片(CXR)诊断中的偏差。材料与方法 在这项回顾性研究中,我们在两个数据集上对所提出的方法进行了评估:医学影像和数据资源中心(MIDRC)数据集,其中包含截至 2023 年 4 月 20 日为 COVID-19 诊断收集的 27,796 名患者的 77,887 张 CXR;以及美国国立卫生研究院胸部 X 光 14(NIH-CXR)数据集,其中包含 1992 年至 2015 年收集的 30,805 名患者的 112,120 张 CXR。在 NIH-CXR 数据集中,胸部异常包括肺不张、心脏肿大、渗出、浸润、肿块、结节、肺炎、气胸、合并症、水肿、肺气肿、纤维化、胸膜增厚或疝气。所提出的方法利用监督对比学习和精心挑选的正负样本生成公平的图像嵌入,并在后续任务中对其进行微调,以减少 CXR 诊断中的偏差。使用接收者工作特征曲线下的边际面积(AUC)差值(ΔmAUC)对该方法进行了评估。结果 经配对 T 检验(P < .001)显示,与基线模型相比,所提出的模型在所有亚组中的偏倚率均显著降低。在 MIDRC 上,所提方法获得的性别、种族和年龄分组的 ΔmAUCs 分别为 0.01(95% CI,0.01-0.01)、0.21(95% CI,0.21-0.21)和 0.10(95% CI,0.10-0.10);在 NIH-CXR 上,性别和年龄分组的 ΔmAUCs 分别为 0.01(95% CI,0.01-0.01)和 0.05(95% CI,0.05-0.05)。结论 采用有监督的对比学习可以减轻 CXR 诊断中的偏差,解决基于深度学习的诊断方法的公平性和可靠性问题。©RSNA,2024。
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Improving Fairness of Automated Chest Radiograph Diagnosis by Contrastive Learning.

Purpose To develop an artificial intelligence model that uses supervised contrastive learning (SCL) to minimize bias in chest radiograph diagnosis. Materials and Methods In this retrospective study, the proposed method was evaluated on two datasets: the Medical Imaging and Data Resource Center (MIDRC) dataset with 77 887 chest radiographs in 27 796 patients collected as of April 20, 2023, for COVID-19 diagnosis and the National Institutes of Health ChestX-ray14 dataset with 112 120 chest radiographs in 30 805 patients collected between 1992 and 2015. In the ChestX-ray14 dataset, thoracic abnormalities included atelectasis, cardiomegaly, effusion, infiltration, mass, nodule, pneumonia, pneumothorax, consolidation, edema, emphysema, fibrosis, pleural thickening, and hernia. The proposed method used SCL with carefully selected positive and negative samples to generate fair image embeddings, which were fine-tuned for subsequent tasks to reduce bias in chest radiograph diagnosis. The method was evaluated using the marginal area under the receiver operating characteristic curve difference (∆mAUC). Results The proposed model showed a significant decrease in bias across all subgroups compared with the baseline models, as evidenced by a paired t test (P < .001). The ∆mAUCs obtained by the proposed method were 0.01 (95% CI: 0.01, 0.01), 0.21 (95% CI: 0.21, 0.21), and 0.10 (95% CI: 0.10, 0.10) for sex, race, and age subgroups, respectively, on the MIDRC dataset and 0.01 (95% CI: 0.01, 0.01) and 0.05 (95% CI: 0.05, 0.05) for sex and age subgroups, respectively, on the ChestX-ray14 dataset. Conclusion Employing SCL can mitigate bias in chest radiograph diagnosis, addressing concerns of fairness and reliability in deep learning-based diagnostic methods. Keywords: Thorax, Diagnosis, Supervised Learning, Convolutional Neural Network (CNN), Computer-aided Diagnosis (CAD) Supplemental material is available for this article. © RSNA, 2024 See also the commentary by Johnson in this issue.

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来源期刊
CiteScore
16.20
自引率
1.00%
发文量
0
期刊介绍: Radiology: Artificial Intelligence is a bi-monthly publication that focuses on the emerging applications of machine learning and artificial intelligence in the field of imaging across various disciplines. This journal is available online and accepts multiple manuscript types, including Original Research, Technical Developments, Data Resources, Review articles, Editorials, Letters to the Editor and Replies, Special Reports, and AI in Brief.
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