{
  "id": 187487,
  "title": "Preprocessed train dataset - 128x128 npy files monochannel",
  "url": "/competitions/rsna-str-pulmonary-embolism-detection/discussion/187487",
  "author_name": "Alex Bader",
  "post_date": "2020-09-29T06:28:18.970000",
  "votes": 3,
  "comment_count": 0,
  "views": 0,
  "content": "<p>I just finished preprocessing of the train dataset to 128px monochannel npy files. Each exam is subsampled to 20 images, giving a constant data cube size of 20x128x128 per study.</p>\n<p>The processing notebooks for the data and the train data table are here</p>\n<p><a href=\"https://www.kaggle.com/spacelx/2020-pe-preprocessing-train-data\" target=\"_blank\">https://www.kaggle.com/spacelx/2020-pe-preprocessing-train-data</a><br>\n<a href=\"https://www.kaggle.com/spacelx/2020-pe-preprocessing-train-table\" target=\"_blank\">https://www.kaggle.com/spacelx/2020-pe-preprocessing-train-table</a></p>\n<p>and the finished dataset can be found here</p>\n<p><a href=\"https://www.kaggle.com/spacelx/2020pe-preprocessed-train-data\" target=\"_blank\">https://www.kaggle.com/spacelx/2020pe-preprocessed-train-data</a></p>\n<p>Have fun playing around with it!</p>",
  "messages": [
    {
      "id": 1030985,
      "postDate": "2020-09-29T06:28:18.970Z",
      "content": "<p>I just finished preprocessing of the train dataset to 128px monochannel npy files. Each exam is subsampled to 20 images, giving a constant data cube size of 20x128x128 per study.</p>\n<p>The processing notebooks for the data and the train data table are here</p>\n<p><a href=\"https://www.kaggle.com/spacelx/2020-pe-preprocessing-train-data\" target=\"_blank\">https://www.kaggle.com/spacelx/2020-pe-preprocessing-train-data</a><br>\n<a href=\"https://www.kaggle.com/spacelx/2020-pe-preprocessing-train-table\" target=\"_blank\">https://www.kaggle.com/spacelx/2020-pe-preprocessing-train-table</a></p>\n<p>and the finished dataset can be found here</p>\n<p><a href=\"https://www.kaggle.com/spacelx/2020pe-preprocessed-train-data\" target=\"_blank\">https://www.kaggle.com/spacelx/2020pe-preprocessed-train-data</a></p>\n<p>Have fun playing around with it!</p>",
      "rawMarkdown": "I just finished preprocessing of the train dataset to 128px monochannel npy files. Each exam is subsampled to 20 images, giving a constant data cube size of 20x128x128 per study.\n\nThe processing notebooks for the data and the train data table are here\n\nhttps://www.kaggle.com/spacelx/2020-pe-preprocessing-train-data\nhttps://www.kaggle.com/spacelx/2020-pe-preprocessing-train-table\n\nand the finished dataset can be found here\n\nhttps://www.kaggle.com/spacelx/2020pe-preprocessed-train-data\n\nHave fun playing around with it!",
      "votes": 3
    }
  ],
  "comments": [],
  "raw_markdown_by_id": {
    "1030985": "I just finished preprocessing of the train dataset to 128px monochannel npy files. Each exam is subsampled to 20 images, giving a constant data cube size of 20x128x128 per study.\n\nThe processing notebooks for the data and the train data table are here\n\nhttps://www.kaggle.com/spacelx/2020-pe-preprocessing-train-data\nhttps://www.kaggle.com/spacelx/2020-pe-preprocessing-train-table\n\nand the finished dataset can be found here\n\nhttps://www.kaggle.com/spacelx/2020pe-preprocessed-train-data\n\nHave fun playing around with it!"
  }
}