{
  "id": "2308.01137",
  "version": "v1",
  "published": "2023-08-02T13:28:44.000Z",
  "updated": "2023-08-02T13:28:44.000Z",
  "title": "Multi-task learning for classification, segmentation, reconstruction, and detection on chest CT scans",
  "authors": [
    "Weronika Hryniewska-Guzik",
    "Maria Kędzierska",
    "Przemysław Biecek"
  ],
  "comment": "presented at the Polish Conference on Artificial Intelligence (PP-RAI), 2023",
  "categories": [
    "eess.IV",
    "cs.CV",
    "cs.LG"
  ],
  "abstract": "Lung cancer and covid-19 have one of the highest morbidity and mortality rates in the world. For physicians, the identification of lesions is difficult in the early stages of the disease and time-consuming. Therefore, multi-task learning is an approach to extracting important features, such as lesions, from small amounts of medical data because it learns to generalize better. We propose a novel multi-task framework for classification, segmentation, reconstruction, and detection. To the best of our knowledge, we are the first ones who added detection to the multi-task solution. Additionally, we checked the possibility of using two different backbones and different loss functions in the segmentation task.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2023-08-02T13:28:44.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "chest ct scans",
      "multi-task learning",
      "classification",
      "reconstruction",
      "novel multi-task framework"
    ],
    "tags": [
      "conference paper"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}