Accurate annotation of fixation type is a critical step in slide preparation for pathology laboratories. However, this manual process is prone to errors, impacting downstream analyses and diagnostic accuracy. Existing methods for verifying formalin-fixed, paraffin-embedded (FFPE), and frozen section (FS) fixation types typically require full-resolution whole-slide images (WSIs), limiting scalability for high-throughput quality control. We propose a deep-learning model to predict fixation types using low-resolution, pre-scan thumbnail images. The model was trained on WSIs from the TUM Institute of Pathology (n=1,200, Leica GT450DX) and evaluated on a class-balanced subset of The Cancer Genome Atlas dataset (TCGA, n=8,800, Leica AT2), as well as on class-balanced datasets from Augsburg (n=695 [392 FFPE, 303 FS], Philips UFS) and Regensburg (n=202, 3DHISTECH P1000). Our model achieves an AUROC of 0.88 on TCGA, outperforming comparable pre-scan methods by 4.8%. It also achieves AUROCs of 0.72 on Regensburg and Augsburg slides, underscoring challenges related to scanner-induced domain shifts. Furthermore, the model processes each slide in 21 ms, $400\times$ faster than existing high-magnification, full-resolution methods, enabling rapid, high-throughput processing. This approach provides an efficient solution for detecting labelling errors without relying on high-magnification scans, offering a valuable tool for quality control in high-throughput pathology workflows. Future work will improve and evaluate the model's generalisation to additional scanner types. Our findings suggest that this method can increase accuracy and efficiency in digital pathology workflows and may be extended to other low-resolution slide annotations.

翻译：准确标注固定类型是病理学实验室切片制备的关键步骤。然而，这一人工操作过程容易出错，影响下游分析和诊断准确性。现有验证福尔马林固定石蜡包埋（FFPE）与冰冻切片（FS）固定类型的方法通常需要全分辨率全玻片图像（WSIs），限制了高通量质量检测的可扩展性。我们提出了一种深度学习模型，利用低分辨率预扫描缩略图图像预测固定类型。该模型使用慕尼黑工业大学病理学研究所的WSIs（n=1,200，Leica GT450DX）进行训练，并在癌症基因组图谱数据集（TCGA，n=8,800，Leica AT2）的类别平衡子集以及奥格斯堡（n=695 [392 FFPE，303 FS]，Philips UFS）和雷根斯堡（n=202，3DHISTECH P1000）的类别平衡数据集上进行评估。我们的模型在TCGA上实现了0.88的AUROC，优于同类预扫描方法4.8%。在雷根斯堡和奥格斯堡切片上也分别实现了0.72的AUROC，凸显了扫描仪引起的域偏移相关挑战。此外，该模型处理每张切片仅需21毫秒，比现有高倍率全分辨率方法快$400\times$，实现了快速、高通量的处理。该方法为在不依赖高倍率扫描的情况下检测标签错误提供了一种高效解决方案，为高通量病理学工作流程中的质量控制提供了有价值的工具。未来工作将改进并评估模型对更多扫描仪类型的泛化能力。我们的研究结果表明，该方法可以提高数字病理学工作流程的准确性和效率，并可扩展至其他低分辨率切片标注任务。