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@ARTICLE{Rehn:1050784,
      author       = {Rehn, Fabian and Pils, Marlene and Bujnicki, Tuyen and
                      Bannach, Oliver and Willbold, Dieter},
      title        = {{A}rtifact detection in fluorescence microscopy using
                      convolutional autoencoder},
      journal      = {Scientific reports},
      volume       = {15},
      number       = {1},
      issn         = {2045-2322},
      address      = {[London]},
      publisher    = {Springer Nature},
      reportid     = {FZJ-2026-00508},
      pages        = {32482},
      year         = {2025},
      abstract     = {To ensure analytical accuracy in fluorescence microscopy
                      image analysis, robust artifact detection is essential. For
                      large datasets or time-sensitive analyses, automation is
                      advisable, as it not only reduces time and costs but also
                      eliminates human bias and enhances reproducibility. Although
                      artificial intelligence is commonly employed for artifact
                      detection, it is typically limited to recognizing artifact
                      types that have been previously learned, often necessitating
                      large training datasets. This study proposes an approach for
                      an automated detection of previously unseen artifacts
                      without the need for a training set of artifact-laden
                      images. Multiple datasets were assembled using images
                      generated by our surface-based intensity distribution
                      analysis (sFIDA) technology during different experiments. A
                      convolutional autoencoder was trained on a dataset of
                      artifact-free images to reproduce preprocessed images
                      accurately. Artifact-laden images are subsequently detected
                      by computing the difference between the input and output of
                      the model, with increased discrepancies indicating the
                      presence of artifacts. The proposed model is capable of
                      classifying artifacts across different datasets with an
                      average accuracy of $95.5\%.$ Additionally, the model was
                      able to detect unseen artifacts of various types, including
                      differences in cause, structure, size and intensity. The
                      findings demonstrate that convolutional autoencoders provide
                      a lightweight, but effective method for detecting
                      artifact-laden images. While the method was tested only on
                      sFIDA images, its design, which does not rely on an artifact
                      specific training set, suggests potential for use across
                      various microscopy techniques.},
      cin          = {IBI-7},
      ddc          = {600},
      cid          = {I:(DE-Juel1)IBI-7-20200312},
      pnm          = {5241 - Molecular Information Processing in Cellular Systems
                      (POF4-524)},
      pid          = {G:(DE-HGF)POF4-5241},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1038/s41598-025-18943-6},
      url          = {https://juser.fz-juelich.de/record/1050784},
}