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On what basis class labels (“ground truth”) get assigned to images heavily depends on the application scenario, sometimes even without visual inspection of the data. Therefore, it can be of interest to evaluate whether distinguishing intrinsic structures exist within the image data. In this study, it is investigated if images from five small-scale endoscopic datasets where class labels were assigned based on domain-specific criteria can be algorithmically clustered into the desired classes. The image classification task is treated as a clustering comparison problem by comparing ground truth labels with clustering results derived from a variety of image representations.

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Intrinsic Correspondence of Classification Ground Truth and Image Content on the Example of Endoscopic Images

  • 会议论文

  • 发表日期：

    2025年

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  • Johannes Schuiki,
  • Andreas Uhl

摘要

On what basis class labels (“ground truth”) get assigned to images heavily depends on the application scenario, sometimes even without visual inspection of the data. Therefore, it can be of interest to evaluate whether distinguishing intrinsic structures exist within the image data. In this study, it is investigated if images from five small-scale endoscopic datasets where class labels were assigned based on domain-specific criteria can be algorithmically clustered into the desired classes. The image classification task is treated as a clustering comparison problem by comparing ground truth labels with clustering results derived from a variety of image representations.