{
  "id": 340646,
  "title": "data pre-processing for RSNA (jpg+npy files)",
  "url": "/competitions/rsna-2022-cervical-spine-fracture-detection/discussion/340646",
  "author_name": "柯慕灵",
  "post_date": "2022-07-30T08:43:29.300000",
  "votes": 7,
  "comment_count": 7,
  "views": 0,
  "content": "<h1>I post a dataset to pre-process for RSNA, if you need it, this is link</h1>\n<h2><a href=\"https://www.kaggle.com/code/handsomeevy/data-pre-processing-for-rsna/notebook\" target=\"_blank\">data pre-processing for RSNA</a></h2>\n<h2>If you use it, the npy is a 3D matrix, you can get every slice in this array. And I give a example in this notebook.↑↑</h2>\n<h2>And if you want to see the segmentations, you can see this notebook <a href=\"https://www.kaggle.com/code/handsomeevy/read-pre-rsna-segmentation\" target=\"_blank\">read pre-RSNA segmentation</a></h2>\n<h3>This is dataset:</h3>\n<p><a href=\"https://www.kaggle.com/datasets/handsomeevy/data-preprocessing-for-rsna-part1\" target=\"_blank\">Part 1 (without train_images)</a><br>\n<a href=\"https://www.kaggle.com/datasets/handsomeevy/data-preprocessing-for-rsna-the-part-2\" target=\"_blank\">Part 2(1000 train_images)</a><br>\n<a href=\"https://www.kaggle.com/datasets/handsomeevy/data-preprocessing-for-rsna-the-part-3\" target=\"_blank\">Part 2(1019 train_images)</a></p>\n<h2>I hope you all enjoy using it</h2>\n<hr>\n<h3>details for dataset</h3>\n<p>train/test images .dcm=&gt;.jpg<br>\nsegmentations    .nii=&gt;.npy </p>",
  "messages": [
    {
      "id": 1876986,
      "postDate": "2022-07-30T08:43:29.300Z",
      "content": "<h1>I post a dataset to pre-process for RSNA, if you need it, this is link</h1>\n<h2><a href=\"https://www.kaggle.com/code/handsomeevy/data-pre-processing-for-rsna/notebook\" target=\"_blank\">data pre-processing for RSNA</a></h2>\n<h2>If you use it, the npy is a 3D matrix, you can get every slice in this array. And I give a example in this notebook.↑↑</h2>\n<h2>And if you want to see the segmentations, you can see this notebook <a href=\"https://www.kaggle.com/code/handsomeevy/read-pre-rsna-segmentation\" target=\"_blank\">read pre-RSNA segmentation</a></h2>\n<h3>This is dataset:</h3>\n<p><a href=\"https://www.kaggle.com/datasets/handsomeevy/data-preprocessing-for-rsna-part1\" target=\"_blank\">Part 1 (without train_images)</a><br>\n<a href=\"https://www.kaggle.com/datasets/handsomeevy/data-preprocessing-for-rsna-the-part-2\" target=\"_blank\">Part 2(1000 train_images)</a><br>\n<a href=\"https://www.kaggle.com/datasets/handsomeevy/data-preprocessing-for-rsna-the-part-3\" target=\"_blank\">Part 2(1019 train_images)</a></p>\n<h2>I hope you all enjoy using it</h2>\n<hr>\n<h3>details for dataset</h3>\n<p>train/test images .dcm=&gt;.jpg<br>\nsegmentations    .nii=&gt;.npy </p>",
      "rawMarkdown": "#I post a dataset to pre-process for RSNA, if you need it, this is link \n## [data pre-processing for RSNA](https://www.kaggle.com/code/handsomeevy/data-pre-processing-for-rsna/notebook)\n\n##If you use it, the npy is a 3D matrix, you can get every slice in this array. And I give a example in this notebook.↑↑\n\n## And if you want to see the segmentations, you can see this notebook [read pre-RSNA segmentation](https://www.kaggle.com/code/handsomeevy/read-pre-rsna-segmentation)\n\n### This is dataset:\n[Part 1 (without train_images)](https://www.kaggle.com/datasets/handsomeevy/data-preprocessing-for-rsna-part1)\n[Part 2(1000 train_images)](https://www.kaggle.com/datasets/handsomeevy/data-preprocessing-for-rsna-the-part-2)\n[Part 2(1019 train_images)](https://www.kaggle.com/datasets/handsomeevy/data-preprocessing-for-rsna-the-part-3)\n\n##  I hope you all enjoy using it\n\n---\n\n###details for dataset\n\ntrain/test images .dcm=>.jpg\nsegmentations    .nii=>.npy ",
      "votes": 7
    },
    {
      "id": 1896756,
      "postDate": "2022-08-13T05:58:41.677Z",
      "content": "<p>Thanks a lot! loading nii was just total nightmare</p>",
      "rawMarkdown": "Thanks a lot! loading nii was just total nightmare",
      "votes": 1,
      "replies": [
        {
          "id": 1896832,
          "postDate": "2022-08-13T07:42:35.890Z",
          "content": "<p>Haha, you are welcome. It's a pleasure to help you<br>\nJust give my notebook a like is OK.</p>",
          "rawMarkdown": "Haha, you are welcome. It's a pleasure to help you\nJust give my notebook a like is OK."
        }
      ]
    },
    {
      "id": 1877865,
      "postDate": "2022-07-31T03:52:27.143Z",
      "content": "<p>Thanks for sharing! <br>\nGreat work as always.</p>",
      "rawMarkdown": "Thanks for sharing! \nGreat work as always.\n\n\n",
      "votes": 1
    },
    {
      "id": 1877165,
      "postDate": "2022-07-30T11:50:07.817Z",
      "content": "<p>Great work! This is likely to be highly useful for all users! <br>\nThanks for sharing the data!</p>",
      "rawMarkdown": "Great work! This is likely to be highly useful for all users! \nThanks for sharing the data!",
      "votes": 1,
      "replies": [
        {
          "id": 1877318,
          "postDate": "2022-07-30T14:29:15.213Z",
          "content": "<p>Thanks, but code is not wrong, the size of train_images is too large, you can download the initial dataset and code to run in your own local environment.😂😂😂<br>\nI have to cut the images size in different dataset</p>",
          "rawMarkdown": "Thanks, but code is not wrong, the size of train_images is too large, you can download the initial dataset and code to run in your own local environment.😂😂😂\nI have to cut the images size in different dataset"
        }
      ]
    },
    {
      "id": 1876988,
      "postDate": "2022-07-30T08:46:18.107Z",
      "content": "<h2>The latest vesion(using tqdm) jupyter notebook is running,and I will post the data after running.😎😎😎</h2>",
      "rawMarkdown": "##The latest vesion(using tqdm) jupyter notebook is running,and I will post the data after running.😎😎😎"
    },
    {
      "id": 1940322,
      "postDate": "2022-09-15T09:35:08.627Z",
      "content": "<p>Thanks for sharing 🙌</p>",
      "rawMarkdown": "Thanks for sharing 🙌",
      "votes": 1
    }
  ],
  "comments": [
    {
      "id": 1896756,
      "author_name": "JunHyeonKwon",
      "author_url": "",
      "post_date": "2022-08-13T05:58:41.677000",
      "content": "<p>Thanks a lot! loading nii was just total nightmare</p>",
      "votes": 1,
      "replies": [
        {
          "id": 1896832,
          "author_name": "柯慕灵",
          "author_url": "",
          "post_date": "2022-08-13T07:42:35.890000",
          "content": "<p>Haha, you are welcome. It's a pleasure to help you<br>\nJust give my notebook a like is OK.</p>",
          "votes": 0,
          "replies": []
        }
      ]
    },
    {
      "id": 1877865,
      "author_name": "The Devastator",
      "author_url": "",
      "post_date": "2022-07-31T03:52:27.143000",
      "content": "<p>Thanks for sharing! <br>\nGreat work as always.</p>",
      "votes": 1,
      "replies": []
    },
    {
      "id": 1877165,
      "author_name": "Ravi Ramakrishnan",
      "author_url": "",
      "post_date": "2022-07-30T11:50:07.817000",
      "content": "<p>Great work! This is likely to be highly useful for all users! <br>\nThanks for sharing the data!</p>",
      "votes": 1,
      "replies": [
        {
          "id": 1877318,
          "author_name": "柯慕灵",
          "author_url": "",
          "post_date": "2022-07-30T14:29:15.213000",
          "content": "<p>Thanks, but code is not wrong, the size of train_images is too large, you can download the initial dataset and code to run in your own local environment.😂😂😂<br>\nI have to cut the images size in different dataset</p>",
          "votes": 0,
          "replies": []
        }
      ]
    },
    {
      "id": 1876988,
      "author_name": "柯慕灵",
      "author_url": "",
      "post_date": "2022-07-30T08:46:18.107000",
      "content": "<h2>The latest vesion(using tqdm) jupyter notebook is running,and I will post the data after running.😎😎😎</h2>",
      "votes": 0,
      "replies": []
    },
    {
      "id": 1940322,
      "author_name": "Jef Jonkers",
      "author_url": "",
      "post_date": "2022-09-15T09:35:08.627000",
      "content": "<p>Thanks for sharing 🙌</p>",
      "votes": 1,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "1876986": "#I post a dataset to pre-process for RSNA, if you need it, this is link \n## [data pre-processing for RSNA](https://www.kaggle.com/code/handsomeevy/data-pre-processing-for-rsna/notebook)\n\n##If you use it, the npy is a 3D matrix, you can get every slice in this array. And I give a example in this notebook.↑↑\n\n## And if you want to see the segmentations, you can see this notebook [read pre-RSNA segmentation](https://www.kaggle.com/code/handsomeevy/read-pre-rsna-segmentation)\n\n### This is dataset:\n[Part 1 (without train_images)](https://www.kaggle.com/datasets/handsomeevy/data-preprocessing-for-rsna-part1)\n[Part 2(1000 train_images)](https://www.kaggle.com/datasets/handsomeevy/data-preprocessing-for-rsna-the-part-2)\n[Part 2(1019 train_images)](https://www.kaggle.com/datasets/handsomeevy/data-preprocessing-for-rsna-the-part-3)\n\n##  I hope you all enjoy using it\n\n---\n\n###details for dataset\n\ntrain/test images .dcm=>.jpg\nsegmentations    .nii=>.npy ",
    "1896756": "Thanks a lot! loading nii was just total nightmare",
    "1877865": "Thanks for sharing! \nGreat work as always.\n\n\n",
    "1877165": "Great work! This is likely to be highly useful for all users! \nThanks for sharing the data!",
    "1876988": "##The latest vesion(using tqdm) jupyter notebook is running,and I will post the data after running.😎😎😎",
    "1940322": "Thanks for sharing 🙌"
  }
}