{
  "annotation_id": "96ae7a9a-018a-4564-9fdd-e4ffb964952d",
  "date_created": "2026-03-02T18:01:17.630000Z",
  "date_modified": "2026-03-02T18:01:17.630000Z",
  "file_hash": "3910d5d269a68ce76fe96a46a5424ff915d7d5f283e084aa9de7cddef6c5676f",
  "private": false,
  "record": {
    "abstract": "A computer-aided detection (CAD) system for the identification of pulmonary\nnodules in low-dose multi-detector helical Computed Tomography (CT) images with\n1.25 mm slice thickness is presented. The basic modules of our lung-CAD system,\na dot-enhancement filter for nodule candidate selection and a neural classifier\nfor false-positive finding reduction, are described. The results obtained on\nthe collected database of lung CT scans are discussed.",
    "arxiv_id": "physics/0701161",
    "authors": [
      "I. Gori",
      "R. Bellotti",
      "P. Cerello",
      "S. C. Cheran",
      "G. De Nunzio",
      "M. E. Fantacci",
      "P. Kasae",
      "G. L. Masala",
      "A. Preite Martinez",
      "A. Retico"
    ],
    "categories": [
      "physics.med-ph"
    ],
    "doi": "10.1109/NSSMIC.2006.353752",
    "title": "Lung Nodule Detection in Screening Computed Tomography",
    "url": "https://arxiv.org/abs/physics/0701161"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "b50a9c74-45c7-453b-9d6e-29a6a27efeb7",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}