{
  "annotation_id": "7cbd47a4-9f8d-46b4-87e4-1496be7f1ab4",
  "date_created": "2026-03-02T18:01:17.176000Z",
  "date_modified": "2026-03-02T18:01:17.176000Z",
  "file_hash": "df75aff421a03193f67c6f96115a643682ba9df6cc1d5200e9c676edbde714a0",
  "private": false,
  "record": {
    "abstract": "A computer-aided detection (CAD) system for the identification of pulmonary\nnodules in low-dose multi-detector helical CT images with 1.25 mm slice\nthickness is being developed in the framework of the INFN-supported MAGIC-5\nItalian project. The basic modules of our lung-CAD system, a dot enhancement\nfilter for nodule candidate selection and a voxel-based neural classifier for\nfalse-positive finding reduction, are described. Preliminary results obtained\non the so-far collected database of lung CT scans are discussed.",
    "arxiv_id": "physics/0701138",
    "authors": [
      "P. Delogu",
      "M. E. Fantacci",
      "I. Gori",
      "A. Preite Martinez",
      "A. Retico"
    ],
    "categories": [
      "physics.med-ph"
    ],
    "title": "Computer-aided detection of pulmonary nodules in low-dose CT",
    "url": "https://arxiv.org/abs/physics/0701138"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "152b5b11-3cbd-4c2a-9510-2fa367b4c51c",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}