{
  "id": 432605,
  "title": "How do you deal with such big files?",
  "url": "/competitions/rsna-2023-abdominal-trauma-detection/discussion/432605",
  "author_name": "Gyula Maloveczky4",
  "post_date": "2023-08-18T06:05:57.074000",
  "votes": 2,
  "comment_count": 4,
  "views": 0,
  "content": "<p>There are 400GB of data. What do you do with it to make it more manageable?</p>",
  "messages": [
    {
      "id": 2401012,
      "postDate": "2023-08-21T11:36:05.667Z",
      "content": "<p>Apart from what has been already discussed in the comments below. You can also resize your images and then save the scan as 3d tensors. This way you reduce the number of files as well as overall datasize. </p>\n<p>You can refer the notebook <a href=\"https://www.kaggle.com/code/harsha1999/dicom-images-3d-tensors\" target=\"_blank\">here</a> for converting dicom scans to 3d tensors.</p>",
      "rawMarkdown": "Apart from what has been already discussed in the comments below. You can also resize your images and then save the scan as 3d tensors. This way you reduce the number of files as well as overall datasize. \n\nYou can refer the notebook [here](https://www.kaggle.com/code/harsha1999/dicom-images-3d-tensors) for converting dicom scans to 3d tensors.",
      "votes": 1
    },
    {
      "id": 2396209,
      "postDate": "2023-08-18T06:05:57.073Z",
      "content": "<p>There are 400GB of data. What do you do with it to make it more manageable?</p>",
      "rawMarkdown": "There are 400GB of data. What do you do with it to make it more manageable?",
      "votes": 1
    },
    {
      "id": 2399215,
      "postDate": "2023-08-20T07:56:58.403Z",
      "content": "<p>You can use preprocessed datasets such as these -</p>\n<ul>\n<li><a href=\"https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/discussion/433004\" target=\"_blank\">https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/discussion/433004</a></li>\n<li><a href=\"https://www.kaggle.com/datasets/awsaf49/rsna-atd-512x512-png-v2-dataset\" target=\"_blank\">https://www.kaggle.com/datasets/awsaf49/rsna-atd-512x512-png-v2-dataset</a> (this one is not a complete dataset though)</li>\n</ul>",
      "rawMarkdown": "You can use preprocessed datasets such as these -\n- https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/discussion/433004\n- https://www.kaggle.com/datasets/awsaf49/rsna-atd-512x512-png-v2-dataset (this one is not a complete dataset though)",
      "votes": 2,
      "replies": [
        {
          "id": 2399229,
          "postDate": "2023-08-20T08:13:26.447Z",
          "content": "<p>Thank you very much</p>",
          "rawMarkdown": "Thank you very much"
        }
      ]
    },
    {
      "id": 2396299,
      "postDate": "2023-08-18T07:26:14.680Z",
      "content": "<p>You can convert dicom files to npy or png format.The file size will be much smaller.<br>\nYou may find this link useful:<br>\n<a href=\"https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/discussion/427427\" target=\"_blank\">https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/discussion/427427</a></p>",
      "rawMarkdown": "You can convert dicom files to npy or png format.The file size will be much smaller.\nYou may find this link useful:\nhttps://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/discussion/427427",
      "votes": 2,
      "isDeleted": true
    }
  ],
  "comments": [
    {
      "id": 2401012,
      "author_name": "Harshanand",
      "author_url": "",
      "post_date": "2023-08-21T11:36:05.667000",
      "content": "<p>Apart from what has been already discussed in the comments below. You can also resize your images and then save the scan as 3d tensors. This way you reduce the number of files as well as overall datasize. </p>\n<p>You can refer the notebook <a href=\"https://www.kaggle.com/code/harsha1999/dicom-images-3d-tensors\" target=\"_blank\">here</a> for converting dicom scans to 3d tensors.</p>",
      "votes": 1,
      "replies": []
    },
    {
      "id": 2399215,
      "author_name": "Priya Nagda",
      "author_url": "",
      "post_date": "2023-08-20T07:56:58.403000",
      "content": "<p>You can use preprocessed datasets such as these -</p>\n<ul>\n<li><a href=\"https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/discussion/433004\" target=\"_blank\">https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/discussion/433004</a></li>\n<li><a href=\"https://www.kaggle.com/datasets/awsaf49/rsna-atd-512x512-png-v2-dataset\" target=\"_blank\">https://www.kaggle.com/datasets/awsaf49/rsna-atd-512x512-png-v2-dataset</a> (this one is not a complete dataset though)</li>\n</ul>",
      "votes": 2,
      "replies": [
        {
          "id": 2399229,
          "author_name": "Gyula Maloveczky4",
          "author_url": "",
          "post_date": "2023-08-20T08:13:26.447000",
          "content": "<p>Thank you very much</p>",
          "votes": 0,
          "replies": []
        }
      ]
    },
    {
      "id": 2396299,
      "author_name": "",
      "author_url": "",
      "post_date": "2023-08-18T07:26:14.680000",
      "content": "<p>You can convert dicom files to npy or png format.The file size will be much smaller.<br>\nYou may find this link useful:<br>\n<a href=\"https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/discussion/427427\" target=\"_blank\">https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/discussion/427427</a></p>",
      "votes": 2,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "2401012": "Apart from what has been already discussed in the comments below. You can also resize your images and then save the scan as 3d tensors. This way you reduce the number of files as well as overall datasize. \n\nYou can refer the notebook [here](https://www.kaggle.com/code/harsha1999/dicom-images-3d-tensors) for converting dicom scans to 3d tensors.",
    "2396209": "There are 400GB of data. What do you do with it to make it more manageable?",
    "2399215": "You can use preprocessed datasets such as these -\n- https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/discussion/433004\n- https://www.kaggle.com/datasets/awsaf49/rsna-atd-512x512-png-v2-dataset (this one is not a complete dataset though)",
    "2396299": "You can convert dicom files to npy or png format.The file size will be much smaller.\nYou may find this link useful:\nhttps://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/discussion/427427"
  }
}