{
  "annotation_id": "c0f365a8-fd86-457b-8881-820c28806c8e",
  "date_created": "2026-03-02T18:01:18.082000Z",
  "date_modified": "2026-03-02T18:01:18.082000Z",
  "file_hash": "f2e5ee3aa0350443dc4ea752c3523dabf591b8384f056129a4c4610a647ffcbc",
  "private": false,
  "record": {
    "abstract": "A computer-aided detection (CADe) system for the identification of\nmicrocalcification clusters in digital mammograms has been developed. It is\nmainly based on the application of wavelet transforms for image filtering and\nneural networks for both the feature extraction and the classification\nprocedures.\n  This CADe system is easily adaptable to different databases. We report and\ncompare the FROC curves obtained on the private database we used for developing\nthe CADe system and on the publicly available MIAS database. The results\nachieved on the two databases show the same trend, thus demonstrating the good\ngeneralization capability of the system.",
    "arxiv_id": "physics/0701243",
    "authors": [
      "P. Delogu",
      "M. E. Fantacci",
      "A. Retico",
      "A. Stefanini",
      "A. Tata"
    ],
    "categories": [
      "physics.med-ph"
    ],
    "journal_ref": "Volume XL. Frontier Science 2005 - New Frontiers in Subnuclear\n  Physics. Eds. A. Pullia and M. Paganoni.",
    "title": "A multi-scale approach to the computer-aided detection of   microcalcification clusters in digital mammograms",
    "url": "https://arxiv.org/abs/physics/0701243"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "290d1a1a-6a56-42c0-9c49-58a1f314dc51",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}