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AutoQC-Bench: a diffusion model and benchmark for automatic quality control in high-throughput microscopy.

Zixuan Pan1, Justin Sonneck2,3, Dennis Nagel4

  • 1Computer Science and Engineering, University of Notre Dame, Notre Dame, USA.

Npj Imaging
|November 7, 2025
PubMed
Summary
This summary is machine-generated.

AutoQC-Bench is new software that automatically detects microscopy image artifacts. It uses a diffusion model and a large benchmark dataset to improve biomedical imaging quality control.

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Area of Science:

  • Biomedical imaging
  • Microscopy
  • Artificial intelligence

Background:

  • High-throughput microscopy is essential for biological research.
  • Image artifacts can compromise the reliability of microscopy data.
  • Current quality control methods struggle with the scale and diversity of bioimaging data.

Purpose of the Study:

  • To develop an automated software tool for detecting artifacts in biomedical images.
  • To create a comprehensive benchmark dataset for evaluating microscopy quality control methods.
  • To enhance the reliability and reproducibility of large-scale bioimaging studies.

Main Methods:

  • Developed AutoQC-Bench, a software utilizing a reconstruction-driven diffusion model.
  • Created a benchmark dataset comprising 8000 microscopy images with common quality issues.
  • Evaluated the software's performance against existing artifact detection methods.

Main Results:

  • AutoQC-Bench effectively flags abnormal images without prior knowledge of artifact types.
  • The software demonstrates superior performance compared to existing methods.
  • The approach shows generalization capabilities across different imaging modalities.

Conclusions:

  • AutoQC-Bench offers a robust solution for automated microscopy quality control.
  • The software and benchmark dataset facilitate large-scale, reliable bioimaging.
  • Open sharing of resources will advance the field of robust microscopy quality control.