{
  "id": 173333,
  "title": "Helpful resources",
  "url": "/competitions/osic-pulmonary-fibrosis-progression/discussion/173333",
  "author_name": "",
  "post_date": "2020-08-08T19:12:48.027812300Z",
  "votes": null,
  "comment_count": 3,
  "views": 0,
  "content": "<p>Can anyone give links to beginner friendly notebooks of competitions for ways to use CT scan data for regression tasks</p>",
  "messages": [
    {
      "id": "963226",
      "postDate": "08/08/2020 19:12:48",
      "content": "<p>Can anyone give links to beginner friendly notebooks of competitions for ways to use CT scan data for regression tasks</p>",
      "rawMarkdown": "Can anyone give links to beginner friendly notebooks of competitions for ways to use CT scan data for regression tasks",
      "votes": null
    },
    {
      "id": "963280",
      "postDate": "08/08/2020 20:55:31",
      "content": "<p>One can solve either using only the numerical approach or using both CT scan data and numerical data. I would suggest to create a baseline model with using only numerical data first and then approach the CT scan data. One website that helped me to understand the CT scan data a lot is <a href=\"https://www.raddq.com/dicom-processing-segmentation-visualization-in-python/\">https://www.raddq.com/dicom-processing-segmentation-visualization-in-python/</a> . Go through this as it nicely explains everything about dicom images</p>",
      "rawMarkdown": "One can solve either using only the numerical approach or using both CT scan data and numerical data. I would suggest to create a baseline model with using only numerical data first and then approach the CT scan data. One website that helped me to understand the CT scan data a lot is https://www.raddq.com/dicom-processing-segmentation-visualization-in-python/ . Go through this as it nicely explains everything about dicom images",
      "votes": null
    },
    {
      "id": "963538",
      "postDate": "08/09/2020 05:41:11",
      "content": "<p><a href=\"https://www.kaggle.com/carlossouza/end-to-end-model-ct-scans-tabular\">https://www.kaggle.com/carlossouza/end-to-end-model-ct-scans-tabular</a>\nThis is a very great notebook for using CT scans</p>",
      "rawMarkdown": "https://www.kaggle.com/carlossouza/end-to-end-model-ct-scans-tabular\nThis is a very great notebook for using CT scans",
      "votes": null
    },
    {
      "id": "971785",
      "postDate": "08/15/2020 21:59:51",
      "content": "<p>Hi,<br>\nThis notebook is has what you are looking for: <a href=\"https://www.kaggle.com/carlossouza/end-to-end-model-ct-scans-tabular\" target=\"_blank\">https://www.kaggle.com/carlossouza/end-to-end-model-ct-scans-tabular</a></p>",
      "rawMarkdown": "Hi,\nThis notebook is has what you are looking for: https://www.kaggle.com/carlossouza/end-to-end-model-ct-scans-tabular",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 971785,
      "author_name": "azzeineaftiss",
      "author_url": "",
      "post_date": "08/15/2020 21:59:51",
      "content": "<p>Hi,<br>\nThis notebook is has what you are looking for: <a href=\"https://www.kaggle.com/carlossouza/end-to-end-model-ct-scans-tabular\" target=\"_blank\">https://www.kaggle.com/carlossouza/end-to-end-model-ct-scans-tabular</a></p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 963280,
      "author_name": "an0utlier",
      "author_url": "",
      "post_date": "08/08/2020 20:55:31",
      "content": "<p>One can solve either using only the numerical approach or using both CT scan data and numerical data. I would suggest to create a baseline model with using only numerical data first and then approach the CT scan data. One website that helped me to understand the CT scan data a lot is <a href=\"https://www.raddq.com/dicom-processing-segmentation-visualization-in-python/\">https://www.raddq.com/dicom-processing-segmentation-visualization-in-python/</a> . Go through this as it nicely explains everything about dicom images</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 963538,
      "author_name": "havinath",
      "author_url": "",
      "post_date": "08/09/2020 05:41:11",
      "content": "<p><a href=\"https://www.kaggle.com/carlossouza/end-to-end-model-ct-scans-tabular\">https://www.kaggle.com/carlossouza/end-to-end-model-ct-scans-tabular</a>\nThis is a very great notebook for using CT scans</p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "963226": "Can anyone give links to beginner friendly notebooks of competitions for ways to use CT scan data for regression tasks",
    "963280": "One can solve either using only the numerical approach or using both CT scan data and numerical data. I would suggest to create a baseline model with using only numerical data first and then approach the CT scan data. One website that helped me to understand the CT scan data a lot is https://www.raddq.com/dicom-processing-segmentation-visualization-in-python/ . Go through this as it nicely explains everything about dicom images",
    "963538": "https://www.kaggle.com/carlossouza/end-to-end-model-ct-scans-tabular\nThis is a very great notebook for using CT scans",
    "971785": "Hi,\nThis notebook is has what you are looking for: https://www.kaggle.com/carlossouza/end-to-end-model-ct-scans-tabular"
  },
  "source": "meta"
}