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Related Concept Videos

Fixation and Sectioning01:03

Fixation and Sectioning

Two basic types of preparation are used to visualize specimens with a light microscope: wet mounts and fixed specimens.
The simplest type of preparation is the wet mount, in which the specimen is placed in a drop of liquid on the slide. A liquid specimen can be directly deposited on the slide using a dropper. Solid specimens, such as skin scraping, can be placed on the slide before adding a drop of liquid to prepare the wet mount. Sometimes the liquid is simply water, but stains are often added...

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Related Experiment Video

Updated: Jul 15, 2026

Automated Dissection Protocol for Tumor Enrichment in Low Tumor Content Tissues
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Tissue-Preserving Artifact Quality Control for Whole-Slide Computational Pathology.

Lin Wei1, Yue Lu1, Yucheng Shi2

  • 1School of Cyber Science and Engineering, Zhengzhou University, No. 100 Science Avenue, Zhengzhou, Henan, 450001, China.

Journal of Imaging Informatics in Medicine
|July 13, 2026
PubMed
Summary

GuardQC is a new computational pathology pipeline that controls artifacts in whole-slide imaging (WSI) while preserving essential tissue. This quality control method improves diagnostic model reliability by minimizing false positives and tissue loss.

Keywords:
Artifact detectionDigital pathologyImaging informaticsQuality controlTissue preservationWhole-slide imaging

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Area of Science:

  • Computational pathology
  • Digital pathology
  • Medical image analysis

Background:

  • Reliable whole-slide imaging (WSI) analysis requires robust diagnostic models and effective image-quality control.
  • Existing high-sensitivity artifact masks can remove relevant tissue, particularly in dense regions, impacting diagnostic accuracy.
  • Artifacts in WSI can be mistaken for benign histology, leading to misinterpretation.

Purpose of the Study:

  • To introduce GuardQC, a conservative preprocessing pipeline for artifact control and tissue preservation in computational pathology.
  • To develop a quality control method that minimizes artifact masking while maintaining tissue integrity.
  • To enhance the reliability of diagnostic models by improving WSI preprocessing.

Main Methods:

  • GuardQC employs a coarse-to-fine strategy using a high-recall proposal network and a dual-stream ResNet-18 verification model.
  • The verification model integrates local texture and tissue context across multiple resolutions after dynamic alignment.
  • Iterative hard negative mining, localized Otsu re-segmentation, and PDE-based inpainting are used for artifact neutralization and tissue preservation.

Main Results:

  • GuardQC achieved 98.99% accuracy and 100% specificity on an expert-curated subset.
  • The pipeline masked significantly less tissue (0.32%) compared to a deep learning baseline (0.94%) in a WSI cohort.
  • Feature ablations demonstrated substantial recovery of representation consistency for various machine learning models.

Conclusions:

  • GuardQC serves as an effective quality-control layer for WSI preprocessing.
  • The method successfully balances artifact control with critical tissue preservation.
  • GuardQC supports scanner-aware analysis and inspectable processing for computational pathology applications.