{
  "annotation_id": "52117692-0780-4954-97b6-6a36a7e56a1d",
  "date_created": "2026-03-02T18:01:17.883000Z",
  "date_modified": "2026-03-02T18:01:17.883000Z",
  "file_hash": "02552055663e65ff81f6099b52a5b1321e85e715e0ecd97d3c0fa97c026b8fdf",
  "private": false,
  "record": {
    "abstract": "We are developing a computer-aided detection (CAD) system for the\nidentification of small pulmonary nodules in screening CT scans. The main\nmodules of our system, i.e. a dot-enhancement filter for nodule candidate\nselection and a neural classifier for false positive finding reduction, are\ndescribed. The preliminary results obtained on the so-far collected database of\nlung CT are discussed.",
    "arxiv_id": "physics/0701245",
    "authors": [
      "P. Delogu",
      "M. E. Fantacci",
      "I. Gori",
      "A. Preite Martinez",
      "A. Retico",
      "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": "Lung nodule detection in low-dose and high-resolution CT scans",
    "url": "https://arxiv.org/abs/physics/0701245"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "1d910a08-562e-4357-bc9e-53729e442ae6",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}