用低分辨率图快速识别病理切片固定类型，提升质检效率。
Deep Learning-Based Fixation Type Prediction for Quality Assurance in Digital Pathology
- 用预览缩略图训练深度模型预测切片固定方式
- 在TCGA数据集上达0.88 AUROC，比现有方法快400倍
- 适合高通量病理流程中的自动化质检，尤其适用于不同扫描仪
准确标注固定类型是病理实验室切片制备的关键步骤。然而，人工标注易出错，影响后续分析与诊断准确性。现有验证福尔马林固定石蜡包埋（FFPE）和冰冻切片（FS）的方法通常需全分辨率整张幻灯片图像（WSIs），限制了高通量质量控制的可扩展性。本文提出一种基于深度学习的模型，仅使用低分辨率预扫缩略图预测固定类型。模型在慕尼黑工业大学病理学研究所数据集（n=1,200，Leica GT450DX）上训练，并在《癌症基因组图谱》（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）的数据集上评估。模型在TCGA上达到0.88 AUROC，优于同类预扫方法4.8%。在雷根斯堡和奥格斯堡数据集上分别取得0.72的AUROC，反映出扫描仪带来的域偏移挑战。该模型每张幻灯片处理仅需21毫秒，比现有高倍率全分辨率方法快400倍，实现快速高通量处理。该方法无需依赖高倍扫描即可检测标签错误，为高通量病理工作流中的质量控制提供有效工具。未来工作将提升模型对更多扫描仪类型的泛化能力。研究结果表明，该方法可提升数字病理流程的准确性与效率，并可拓展至其他低分辨率切片标注任务。
原文摘要 · Abstract (English)
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.
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