{
  "annotation_id": "12b36380-9054-46c4-bafc-28ce5e8103bc",
  "date_created": "2026-03-02T18:01:18.027000Z",
  "date_modified": "2026-03-02T18:01:18.027000Z",
  "file_hash": "bf37dde65deeaf55c7c7e82c0a2991d67bcc0d517c5fee168089f4c1ef2ddf4e",
  "private": false,
  "record": {
    "abstract": "A computer-aided detection (CADe) system for microcalcification cluster\nidentification in mammograms has been developed in the framework of the\nEU-founded MammoGrid project. The CADe software is mainly based on wavelet\ntransforms and artificial neural networks. It is able to identify\nmicrocalcifications in different datasets of mammograms (i.e. acquired with\ndifferent machines and settings, digitized with different pitch and bit depth\nor direct digital ones). The CADe can be remotely run from GRID-connected\nacquisition and annotation stations, supporting clinicians from geographically\ndistant locations in the interpretation of mammographic data. We report and\ndiscuss the system performances on different datasets of mammograms and the\nstatus of the GRID-enabled CADe analysis.",
    "arxiv_id": "physics/0701084",
    "authors": [
      "P. Delogu",
      "M. E. Fantacci",
      "A. Preite Martinez",
      "A. Retico",
      "A. Stefanini",
      "A. Tata"
    ],
    "categories": [
      "physics.med-ph"
    ],
    "doi": "10.1109/NSSMIC.2005.1596609",
    "journal_ref": "Nuclear Science Symposium Conference Record, 2005 IEEE, Volume 3,\n  23-29 Oct. 2005, 5 pp",
    "title": "A scalable system for microcalcification cluster automated detection in   a distributed mammographic database",
    "url": "https://arxiv.org/abs/physics/0701084"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "60d43f42-9c9b-427d-bd07-4e0f28105d0f",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}