@inproceedings{gayen-etal-2026-medgore,
    title = "{M}ed{G}ore: An Approach and a Dataset for Identification of Sensitive Medical Images",
    author = "Gayen, Soumya  and
      Mulcahey, Rory  and
      Loane, Russell  and
      Demner-Fushman, Dina  and
      Gupta, Deepak",
    editor = "Gupta, Deepak  and
      Thompson, Paul  and
      Ananiadou, Sophia  and
      Demner-Fushman, Dina",
    booktitle = "Proceedings of the Third Workshop on Patient-Oriented Language Processing ({CL}4{H}ealth) @ {LREC} 2026",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/test-year-match/2026.cl4health-1.26/",
    doi = "10.63317/5f7uiuto4ph5",
    pages = "296--306",
    abstract = "Medical images are invaluable in illustrating health issues for the patients. While biomedical publications are a good source of such images, some of the images are not appropriate for the patient viewing without a warning. To enable development of automated tools for selection of patient-safe images and generation of warnings, we created a dataset MedGore of over 78,000 sensitive medical images and 183,000 non-sensitive images published in the biomedical literature. The sensitive content includes gore, severe disease, nudity, surgical openings, internal organs, and other medical images of this nature. The set of the manually identified seed 300 images was expanded using a combination of human curation and a nearest neighbor clustering algorithm. The quality of the automatically labeled images was evaluated manually, yielding a total of more than 4,000 doubly-manually annotated images. The automatically labeled images proved to approach the utility of the manually labeled images for training the models in our experiments that validated the dataset in the task of labeling unseen images using the image features, the figure captions or both."
}