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Obtaining dense annotations for histopathological whole-slide images (WSI), such as segmentation masks or mitotic figure identification, is a labor intensive process due to the large image size and the extensive manual effort required for annotation. Identifying informative regions in WSIs for annotation while leaving other regions unlabeled can significantly reduce the annotation effort. These selected annotation regions should contain valuable training information that allows proper model training without significantly impacting performance compared to full annotation.

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Abstract: Leveraging Image Captions for Selective Whole Slide Image Annotation

  • 会议论文

  • 发表日期：

    2025年

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  • Jingna Qiu,
  • Marc Aubreville,
  • Frauke Wilm,
  • Mathias Öttl,
  • Jonas Utz,
  • Maja Schlereth,
  • Katharina Breininger

摘要

Obtaining dense annotations for histopathological whole-slide images (WSI), such as segmentation masks or mitotic figure identification, is a labor intensive process due to the large image size and the extensive manual effort required for annotation. Identifying informative regions in WSIs for annotation while leaving other regions unlabeled can significantly reduce the annotation effort. These selected annotation regions should contain valuable training information that allows proper model training without significantly impacting performance compared to full annotation.