{
  "annotation_id": "765a26cf-aa3e-4e37-8eb9-20a0c7a4e2fd",
  "date_created": "2026-03-02T18:01:17.892000Z",
  "date_modified": "2026-03-02T18:01:17.892000Z",
  "file_hash": "b7a42ceee41213be47e38375d4699d233e8a3de7d32ab868a2ce301bf2f8ee7e",
  "private": false,
  "record": {
    "abstract": "Evaluating the degree of malignancy of a massive lesion on the basis of the\nmere visual analysis of the mammogram is a non-trivial task. We developed a\nsemi-automated system for massive-lesion characterization with the aim to\nsupport the radiological diagnosis. A dataset of 226 masses has been used in\nthe present analysis. The system performances have been evaluated in terms of\nthe area under the ROC curve, obtaining A_z=0.80+-0.04.",
    "arxiv_id": "physics/0701244",
    "authors": [
      "P. Delogu",
      "M. E. Fantacci",
      "P. Kasae",
      "A. Retico"
    ],
    "categories": [
      "physics.med-ph"
    ],
    "journal_ref": "Volume XL. Frontier Science 2005 - New Frontiers in Subnuclear\n  Physics. Eds. A. Pullia and M. Paganoni.",
    "title": "An automatic system to discriminate malignant from benign massive   lesions in mammograms",
    "url": "https://arxiv.org/abs/physics/0701244"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "01801c4e-d60d-43bf-80ff-8315ab5bb616",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}