Deep Learning-Based Fixation Type Prediction for Quality Assurance in Digital Pathology

• URL: http://arxiv.org/abs/2602.08652v2
• Date: Fri, 13 Feb 2026 14:14:09 GMT
• Title: Deep Learning-Based Fixation Type Prediction for Quality Assurance in Digital Pathology
• Authors: Oskar Thaeter, Tanja Niedermair, Jan E. G. Albin, Johannes Raffler, Ralf Huss, Peter J. Schüffler,
• Abstract summary: We propose a deep-learning model to predict fixation types using low-resolution, pre-scan thumbnail images.<n>The model processes each slide in 21 ms, $400times$ faster than existing high-magnification, full-resolution methods.<n>Our findings suggest that this method can increase accuracy and efficiency in digital pathology and may be extended to other low-resolution slide annotations.
• Score: 0.1570077740602354
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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