{
  "id": 342832,
  "title": "Small size image dataset",
  "url": "/competitions/rsna-2022-cervical-spine-fracture-detection/discussion/342832",
  "author_name": "",
  "post_date": "2022-08-09T01:19:36.405839300Z",
  "votes": 5,
  "comment_count": 2,
  "views": 0,
  "content": "<p>If anyone has created  small size  image dataset for this competition then please post the link in the comments</p>",
  "messages": [
    {
      "id": "1890684",
      "postDate": "08/09/2022 01:19:36",
      "content": "<p>If anyone has created  small size  image dataset for this competition then please post the link in the comments</p>",
      "rawMarkdown": "If anyone has created  small size  image dataset for this competition then please post the link in the comments",
      "votes": null
    },
    {
      "id": "1890689",
      "postDate": "08/09/2022 01:29:45",
      "content": "<p>There are 3 or 4 other topics where others have posted image datasets.</p>",
      "rawMarkdown": "There are 3 or 4 other topics where others have posted image datasets.",
      "votes": null
    },
    {
      "id": "1922527",
      "postDate": "09/01/2022 14:50:53",
      "content": "<p>Hello,<br>\nI have worked on that. Unfortunately, I was not able to make it smaller that 30 Gb. I have reduced the resolution to 1 mm and cut them to 200x200x200 mm in all dimensions. I have also performed some basic data preprocessing.</p>\n<p>Here is my notebook with the code to create this dataset: <a href=\"https://www.kaggle.com/code/ikorol/creating-data-set-with-preprocessed-dcm-images\" target=\"_blank\">https://www.kaggle.com/code/ikorol/creating-data-set-with-preprocessed-dcm-images</a></p>\n<p>If you run this code locally, you will get a data set which is approx 30 Gb large.</p>",
      "rawMarkdown": "Hello,\nI have worked on that. Unfortunately, I was not able to make it smaller that 30 Gb. I have reduced the resolution to 1 mm and cut them to 200x200x200 mm in all dimensions. I have also performed some basic data preprocessing.\n\nHere is my notebook with the code to create this dataset: https://www.kaggle.com/code/ikorol/creating-data-set-with-preprocessed-dcm-images\n\nIf you run this code locally, you will get a data set which is approx 30 Gb large.",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 1890689,
      "author_name": "brandenkmurray",
      "author_url": "",
      "post_date": "08/09/2022 01:29:45",
      "content": "<p>There are 3 or 4 other topics where others have posted image datasets.</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 1922527,
      "author_name": "ikorol",
      "author_url": "",
      "post_date": "09/01/2022 14:50:53",
      "content": "<p>Hello,<br>\nI have worked on that. Unfortunately, I was not able to make it smaller that 30 Gb. I have reduced the resolution to 1 mm and cut them to 200x200x200 mm in all dimensions. I have also performed some basic data preprocessing.</p>\n<p>Here is my notebook with the code to create this dataset: <a href=\"https://www.kaggle.com/code/ikorol/creating-data-set-with-preprocessed-dcm-images\" target=\"_blank\">https://www.kaggle.com/code/ikorol/creating-data-set-with-preprocessed-dcm-images</a></p>\n<p>If you run this code locally, you will get a data set which is approx 30 Gb large.</p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "1890684": "If anyone has created  small size  image dataset for this competition then please post the link in the comments",
    "1890689": "There are 3 or 4 other topics where others have posted image datasets.",
    "1922527": "Hello,\nI have worked on that. Unfortunately, I was not able to make it smaller that 30 Gb. I have reduced the resolution to 1 mm and cut them to 200x200x200 mm in all dimensions. I have also performed some basic data preprocessing.\n\nHere is my notebook with the code to create this dataset: https://www.kaggle.com/code/ikorol/creating-data-set-with-preprocessed-dcm-images\n\nIf you run this code locally, you will get a data set which is approx 30 Gb large."
  },
  "source": "meta"
}