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<p>Reliable biomedical imaging demands rigorous quality control, yet high-throughput microscopy remains prone to diverse artifacts. We present AutoQC-Bench, a software based on a reconstruction-driven diffusion model flagging abnormal images without prior knowledge, and along with a benchmark of 8000 images capturing common quality issues. The software outperforms existing methods, generalizes across modalities, and supports large-scale bioimaging studies. The software and benchmark are openly shared to advance robust microscopy quality control.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月7日

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  • Zixuan Pan,
  • Justin Sonneck,
  • Dennis Nagel,
  • Anja Hasenberg,
  • Matthias Gunzer,
  • Yiyu Shi,
  • Jianxu Chen

摘要

Reliable biomedical imaging demands rigorous quality control, yet high-throughput microscopy remains prone to diverse artifacts. We present AutoQC-Bench, a software based on a reconstruction-driven diffusion model flagging abnormal images without prior knowledge, and along with a benchmark of 8000 images capturing common quality issues. The software outperforms existing methods, generalizes across modalities, and supports large-scale bioimaging studies. The software and benchmark are openly shared to advance robust microscopy quality control.