In numerous studies, deep learning algorithms have proven their potential for the analysis of histopathology images, for example, for revealing the subtypes of tumors or the primary origin of metastases. These models require large datasets for training, which must be anonymized to prevent possible patient identity leaks. This study demonstrates that even relatively simple deep learning algorithms can re-identify patients in large histopathology datasets with substantial accuracy. In addition, we compared a comprehensive set of state-of-the-art whole slide image classifiers and feature extractors for the given task. We evaluated our algorithms on two TCIA datasets including lung squamous cell carcinoma (LSCC) and lung adenocarcinoma (LUAD). We also demonstrate the algorithm’s performance on an in-house dataset of meningioma tissue. We predicted the source patient of a slide with F1 scores of up to 80.1% and 77.19% on the LSCC and LUAD datasets, respectively, and with 77.09% on our meningioma dataset. Based on our findings, we formulated a risk assessment scheme to estimate the risk to the patient’s privacy prior to publication.

中文翻译：

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从组织病理学图像中重新识别


在大量研究中，深度学习算法已经证明了它们在分析组织病理学图像方面的潜力，例如揭示肿瘤的亚型或转移的主要起源。这些模型需要大量数据集进行训练，这些数据集必须匿名，以防止可能的患者身份泄露。这项研究表明，即使相对简单的深度学习算法也可以非常准确地重新识别大型组织病理学数据集中的患者。此外，我们还针对给定任务比较了一组最先进的整个幻灯片图像分类器和特征提取器。我们在两个 TCIA 数据集（包括肺鳞状细胞癌 (LSCC) 和肺腺癌 (LUAD)）上评估了我们的算法。我们还在脑膜瘤组织的内部数据集上展示了该算法的性能。我们预测幻灯片的源患者在 LSCC 和 LUAD 数据集上的 F1 分数分别高达 80.1% 和 77.19%，在我们的脑膜瘤数据集上预测为 77.09%。根据我们的发现，我们制定了风险评估方案，以在发布之前评估患者隐私的风险。