{
  "id": "1904.01638",
  "version": "v1",
  "published": "2019-04-02T19:38:34.000Z",
  "updated": "2019-04-02T19:38:34.000Z",
  "title": "A Strong Baseline for Domain Adaptation and Generalization in Medical Imaging",
  "authors": [
    "Li Yao",
    "Jordan Prosky",
    "Ben Covington",
    "Kevin Lyman"
  ],
  "comment": "Extended abstract of a journal submission",
  "categories": [
    "cs.CV",
    "cs.AI",
    "stat.ML"
  ],
  "abstract": "This work provides a strong baseline for the problem of multi-source multi-target domain adaptation and generalization in medical imaging. Using a diverse collection of ten chest X-ray datasets, we empirically demonstrate the benefits of training medical imaging deep learning models on varied patient populations for generalization to out-of-sample domains.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-04-02T19:38:34.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "strong baseline",
      "generalization",
      "imaging deep learning models",
      "medical imaging deep learning",
      "multi-source multi-target domain adaptation"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}