用扩散模型检测病理切片中的伪影，无需标注也能识别新类型伪影。
DiffusionQC: Artifact Detection in Histopathology via Diffusion Model
- 基于扩散模型将干净图像作为分布基准，异常即为伪影。
- 仅需干净图像训练，在跨染色场景下表现优于现有方法。
- 适合缺乏标注数据的病理图像质量控制场景。
数字病理在现代医学中至关重要，为疾病诊断、预后和治疗提供关键信息。然而，组织切片在制备和数字化过程中常引入伪影，检测并排除这些伪影对保障后续分析可靠性至关重要。传统监督模型通常需要大量带标注的数据，成本高且难以泛化到新型伪影。为此，我们提出 DiffusionQC，利用扩散模型将伪影识别为与干净图像的异常偏离，仅需一组干净图像进行训练，无需像素级标注或预定义伪影类别。此外，我们引入对比学习模块，显式扩大伪影与干净图像分布间的差异，进一步提升性能。实验证明，该方法在多种染色条件下均优于当前最优模型，且所需数据和标注显著更少。
原文摘要 · Abstract (English)
Digital pathology plays a vital role across modern medicine, offering critical insights for disease diagnosis, prognosis, and treatment. However, histopathology images often contain artifacts introduced during slide preparation and digitization. Detecting and excluding them is essential to ensure reliable downstream analysis. Traditional supervised models typically require large annotated datasets, which is resource-intensive and not generalizable to novel artifact types. To address this, we propose DiffusionQC, which detects artifacts as outliers among clean images using a diffusion model. It requires only a set of clean images for training rather than pixel-level artifact annotations and predefined artifact types. Furthermore, we introduce a contrastive learning module to explicitly enlarge the distribution separation between artifact and clean images, yielding an enhanced version of our method. Empirical results demonstrate superior performance to state-of-the-art and offer cross-stain generalization capacity, with significantly less data and annotations.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。