{
 "id": "msancheza/microCube-Duke-Breast-MRI",
 "owner": "msancheza",
 "name": "microCube-Duke-Breast-MRI",
 "downloads": 369,
 "likes": 1,
 "last_modified": "2026-05-10T05:21:00.000Z",
 "stated_licence": "cc-by-4.0",
 "stated_from": "tag",
 "licence_name": null,
 "licence_link": null,
 "file_licence": null,
 "licence_files": [],
 "sources": [
  {
   "id": "tcia-duke-breast-cancer",
   "licence": null,
   "found": false,
   "evidence": "https://huggingface.co/datasets/tcia-duke-breast-cancer"
  }
 ],
 "gated": false,
 "tasks": [
  "image-classification"
 ],
 "citation": "This dataset is a processed derivative of the **Duke Breast Cancer MRI** cohort, originally hosted on **The Cancer Imaging Archive (TCIA)**.\n\n- Original dataset: [Breast-Cancer-MRI-minus-DICOM](https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=70226903)\n- Original citation: Saha, A., Harowicz, M. R., Grimm, L. J., Kim, C. E., Mazurowski, M. A. (2018). *A machine learning approach to radiogenomics of breast cancer: a study of 922 patients and a comparison with 110 gene signatures.* JCO Precision Oncology.\n\nPlease cite the original TCIA/Duke dataset and this processed representation dataset when using these tensors.",
 "signal": "unclear",
 "reason": "The checks could not identify a recognised licence ID for at least one declared source dataset.",
 "revision": "80c89c006892db2ed57b9728c27b26524466b237",
 "stated_evidence": "https://huggingface.co/datasets/msancheza/microCube-Duke-Breast-MRI/blob/80c89c006892db2ed57b9728c27b26524466b237/README.md",
 "file_evidence": "https://huggingface.co/datasets/msancheza/microCube-Duke-Breast-MRI/tree/80c89c006892db2ed57b9728c27b26524466b237",
 "card_nc": false
}