用自监督学习自动检测药物安全评估中的肾异常，大幅减少人工审片工作量。
Self-supervised large-scale kidney abnormality detection in drug safety assessment studies
- 基于UNI基础模型特征，采用自监督方法提升异常检测能力。
- 模型AUC达0.62，阴性预测值89%，可有效识别正常切片。
- 适合药物研发中大规模病理图像筛查，降低人力成本。
肾异常检测是所有临床前药物开发的必要环节，需耗时耗力地审查每项药物安全性研究中的数百至数千张全切片图像，以发现可能提示毒性的细微变化。本研究首次提出适用于肾毒性病理学的大规模自监督异常检测模型，覆盖158种化合物的药物安全评估研究。我们利用UNI基础模型提取特征，发现仅用k近邻分类器在这些特征上表现接近随机水平，表明基础模型特征不足以直接检测异常。随后，通过在同一特征上应用自监督方法，模型性能显著提升，达到AUC 0.62和89%的阴性预测值。该模型未来可用于排除正常切片，显著降低药物开发的时间与成本。
原文摘要 · Abstract (English)
Kidney abnormality detection is required for all preclinical drug development. It involves a time-consuming and costly examination of hundreds to thousands of whole-slide images per drug safety study, most of which are normal, to detect any subtle changes indicating toxic effects. In this study, we present the first large-scale self-supervised abnormality detection model for kidney toxicologic pathology, spanning drug safety assessment studies from 158 compounds. We explore the complexity of kidney abnormality detection on this scale using features extracted from the UNI foundation model (FM) and show that a simple k-nearest neighbor classifier on these features performs at chance, demonstrating that the FM-generated features alone are insufficient for detecting abnormalities. We then demonstrate that a self-supervised method applied to the same features can achieve better-than-chance performance, with an area under the receiver operating characteristic curve of 0.62 and a negative predictive value of 89%. With further development, such a model can be used to rule out normal slides in drug safety assessment studies, reducing the costs and time associated with drug development.
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