{
  "annotation_id": "c959e8eb-456f-4d13-a7c0-54ce6922d6dc",
  "date_created": "2026-03-02T18:01:00.473000Z",
  "date_modified": "2026-03-02T18:01:00.473000Z",
  "file_hash": "ff80183b1321492b38f59d553819d14a6ddcc78dcba0c2287621986f2cdad5b1",
  "private": false,
  "record": {
    "abstract": "The use of automatic systems in the analysis of medical images has proven to\nbe very useful to radiologists, especially in the framework of screening\nprograms, in which radiologists make their first diagnosis on the basis of\nimages only, most of those corresponding to healthy patients, and have to\ndistinguish pathological findings from non-pathological ones at an early stage.\nIn particular, we are developing preprocessing methods to be applied for\npulmonary nodule Computer Aided Detection in low-dose lung Multi Slice CT\n(Computed Tomography) images.",
    "arxiv_id": "physics/0507153",
    "authors": [
      "S. C. Cheran",
      "P. Delogu",
      "I. De Mitri",
      "G. De Nunzio",
      "M. E. Fantacci",
      "F. Fauci",
      "G. Gargano",
      "E. Lopez Torres",
      "R. Massafra",
      "P. Oliva",
      "A. Preite Martinez",
      "G. Raso",
      "A. Retico",
      "S. Stumbo",
      "A. Tata"
    ],
    "categories": [
      "physics.med-ph"
    ],
    "journal_ref": "International Congress Series 1281 (2005) 1099-1103",
    "title": "Pre-processing methods for nodule detection in lung CT",
    "url": "https://arxiv.org/abs/physics/0507153"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "70c3daad-7ef7-4726-b2d6-63118b9ca7c4",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}