{
 "id": "Ehsan-rmz/lgg-mri-segmentation-research",
 "owner": "Ehsan-rmz",
 "name": "lgg-mri-segmentation-research",
 "downloads": 5170,
 "likes": 1,
 "last_modified": "2026-01-02T12:02:05.000Z",
 "stated_licence": "cc-by-nc-sa-4.0",
 "stated_from": "tag",
 "licence_name": null,
 "licence_link": null,
 "file_licence": null,
 "licence_files": [],
 "sources": [],
 "gated": false,
 "tasks": [
  "image-segmentation",
  "image-classification"
 ],
 "citation": "If you use this dataset in your research, please cite the original publications:\n\n```bibtex\n@article{buda2019association,\n  title={Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm},\n  author={Buda, Mateusz and Saha, Ashirbani and Mazurowski, Maciej A},\n  journal={Computers in Biology and Medicine},\n  volume={109},\n  pages={218--225},\n  year={2019},\n  publisher={Elsevier},\n  doi={10.1016/j.compbiomed.2019.05.002}\n}\n\n@article{mazurowski2017radiogenomics,\n  title={Radiogenomics of lower-grade glioma: algorithmically-assessed tumor shape is associated with tumor genomic subtypes and patient outcomes in a multi-institutional study with The Cancer Genome Atlas data},\n  author={Mazurowski, Maciej A and Clark, Kal and Czarnek, Nicholas M and Shamsesfandabadi, Parisa and Peters, Katherine B and Saha, Ashirbani},\n  journal={Journal of Neuro-Oncology},\n  volume={133},\n  number={1},\n  pages={27--35},\n  year={2017},\n  publisher={Springer},\n  doi={10.1007/s11060-017-2420-1}\n}",
 "signal": "restrictive",
 "reason": "The declared licence (cc-by-nc-sa-4.0) is classified as having use restrictions.",
 "revision": "abb4b77652eb70734d4bbb5634726e01af05185b",
 "stated_evidence": "https://huggingface.co/datasets/Ehsan-rmz/lgg-mri-segmentation-research/blob/abb4b77652eb70734d4bbb5634726e01af05185b/README.md",
 "file_evidence": "https://huggingface.co/datasets/Ehsan-rmz/lgg-mri-segmentation-research/tree/abb4b77652eb70734d4bbb5634726e01af05185b",
 "card_nc": false
}