{
  "id": "1804.04595",
  "version": "v1",
  "published": "2018-04-09T19:40:02.000Z",
  "updated": "2018-04-09T19:40:02.000Z",
  "title": "Assessment of Breast Cancer Histology using Densely Connected Convolutional Networks",
  "authors": [
    "Matthias Kohl",
    "Christoph Walz",
    "Florian Ludwig",
    "Stefan Braunewell",
    "Maximilian Baust"
  ],
  "categories": [
    "cs.CV"
  ],
  "abstract": "Breast cancer is the most frequently diagnosed cancer and leading cause of cancer-related death among females worldwide. In this article, we investigate the applicability of densely connected convolutional neural networks to the problems of histology image classification and whole slide image segmentation in the area of computer-aided diagnoses for breast cancer. To this end, we study various approaches for transfer learning and apply them to the data set from the 2018 grand challenge on breast cancer histology images (BACH).",
  "revisions": [
    {
      "version": "v1",
      "updated": "2018-04-09T19:40:02.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "densely connected convolutional networks",
      "assessment",
      "breast cancer histology images",
      "slide image segmentation",
      "densely connected convolutional neural networks"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}