{
 "id": "JonathanFhima/REYIA",
 "owner": "JonathanFhima",
 "name": "REYIA",
 "downloads": 86,
 "likes": 1,
 "last_modified": "2026-10-17T18:03:58.000Z",
 "stated_licence": "other",
 "stated_from": "tag",
 "licence_name": "apache-2.0-plus-third-party",
 "licence_link": "LICENSE",
 "file_licence": "apache-2.0",
 "licence_files": [
  "LICENSE"
 ],
 "sources": [],
 "gated": "auto",
 "tasks": [
  "image-segmentation"
 ],
 "citation": "If you use REYIA, please cite the paper it was introduced in:\n\n> Fhima, J., Van Eijgen, J., Beeckmans, L., Jacob, T., Freiman, M.,\n> Nakayama, L.F., Stalmans, I., Baskin, C., Behar, J.A. *Enhancing Retinal\n> Vessel Segmentation Generalization via Layout-Aware Generative\n> Modelling.* arXiv:2503.01190, 2025. https://arxiv.org/abs/2503.01190\n\nREYIA's annotations were produced with [Lirot.ai](https://www.aimlab-technion.com/lirot-ai).\nPlease also cite the paper that introduced it:\n\n> Fhima, J., Van Eijgen, J., Freiman, M., Stalmans, I., Behar, J.A. *Lirot.ai:\n> A Novel Platform for Crowd-Sourcing Retinal Image Segmentations.* 2022\n> Computing in Cardiology (CinC), vol. 498, pp. 1-4, IEEE, 2022.\n> arXiv:2208.10100. https://arxiv.org/abs/2208.10100\n\nCode (RLAD): https://github.com/aim-lab/RLAD\n\nPlease also cite the original dataset(s) any images you use come from, per\ntheir own citation requirements (see the Ethical Consent section above).",
 "signal": "conflict",
 "reason": "The custom licence described in the dataset card and the licence detected in a repository file (apache-2.0) fall into different categories, so the licences may conflict.",
 "revision": "42ca60a99acf0c29463cc724f1b1ab216f487e9c",
 "stated_evidence": "https://huggingface.co/datasets/JonathanFhima/REYIA/blob/42ca60a99acf0c29463cc724f1b1ab216f487e9c/README.md",
 "file_evidence": "https://huggingface.co/datasets/JonathanFhima/REYIA/blob/42ca60a99acf0c29463cc724f1b1ab216f487e9c/LICENSE",
 "card_nc": true
}