{
  "id": 213976,
  "title": "Metadata of dicom files",
  "url": "/competitions/vinbigdata-chest-xray-abnormalities-detection/discussion/213976",
  "author_name": "BryanB",
  "post_date": "2021-01-24T23:35:42.424000",
  "votes": 3,
  "comment_count": 0,
  "views": 0,
  "content": "<p>Hello everyone,</p>\n<p>This topic aims at giving some help for those who want to quickly get started and hence avoiding spending some time extracting metadata information from dicom files.</p>\n<h2>Context</h2>\n<p>Each DICOM file contains an array representing the pixel values of the image. However, it also contains resourceful information that could help to have a better understanding of the overall data. This dataset is the result of the extraction of all metadata contained in each DICOM file located in both train and test folders.</p>\n<p>You can check out the data <a href=\"https://www.kaggle.com/bryanb/vinbigdata-chestxray-metadata\" target=\"_blank\">here</a><br>\nAlso this notebook illustrates how I retrieved these dicom metadata, coming along with an EDA. <a href=\"https://www.kaggle.com/bryanb/vinbigdata-chest-x-ray-eda\" target=\"_blank\">EDA notebook</a></p>\n<h2>Content</h2>\n<p>train_dicom_metadata.csv : 15000 rows gathering metadata from dicom files located in train folder<br>\ntest_dicom_metadata.csv : 3000 rows gathering metadata from dicom files located in test folder</p>\n<p>Hope this can help 😉</p>",
  "messages": [
    {
      "id": 1168403,
      "postDate": "2021-01-24T23:35:42.423Z",
      "content": "<p>Hello everyone,</p>\n<p>This topic aims at giving some help for those who want to quickly get started and hence avoiding spending some time extracting metadata information from dicom files.</p>\n<h2>Context</h2>\n<p>Each DICOM file contains an array representing the pixel values of the image. However, it also contains resourceful information that could help to have a better understanding of the overall data. This dataset is the result of the extraction of all metadata contained in each DICOM file located in both train and test folders.</p>\n<p>You can check out the data <a href=\"https://www.kaggle.com/bryanb/vinbigdata-chestxray-metadata\" target=\"_blank\">here</a><br>\nAlso this notebook illustrates how I retrieved these dicom metadata, coming along with an EDA. <a href=\"https://www.kaggle.com/bryanb/vinbigdata-chest-x-ray-eda\" target=\"_blank\">EDA notebook</a></p>\n<h2>Content</h2>\n<p>train_dicom_metadata.csv : 15000 rows gathering metadata from dicom files located in train folder<br>\ntest_dicom_metadata.csv : 3000 rows gathering metadata from dicom files located in test folder</p>\n<p>Hope this can help 😉</p>",
      "rawMarkdown": "Hello everyone,\n\nThis topic aims at giving some help for those who want to quickly get started and hence avoiding spending some time extracting metadata information from dicom files.\n\n## Context\n\nEach DICOM file contains an array representing the pixel values of the image. However, it also contains resourceful information that could help to have a better understanding of the overall data. This dataset is the result of the extraction of all metadata contained in each DICOM file located in both train and test folders.\n\nYou can check out the data [here](https://www.kaggle.com/bryanb/vinbigdata-chestxray-metadata)\nAlso this notebook illustrates how I retrieved these dicom metadata, coming along with an EDA. [EDA notebook](https://www.kaggle.com/bryanb/vinbigdata-chest-x-ray-eda)\n\n## Content\n\ntrain_dicom_metadata.csv : 15000 rows gathering metadata from dicom files located in train folder\ntest_dicom_metadata.csv : 3000 rows gathering metadata from dicom files located in test folder\n\nHope this can help 😉\n",
      "votes": 3
    }
  ],
  "comments": [],
  "raw_markdown_by_id": {
    "1168403": "Hello everyone,\n\nThis topic aims at giving some help for those who want to quickly get started and hence avoiding spending some time extracting metadata information from dicom files.\n\n## Context\n\nEach DICOM file contains an array representing the pixel values of the image. However, it also contains resourceful information that could help to have a better understanding of the overall data. This dataset is the result of the extraction of all metadata contained in each DICOM file located in both train and test folders.\n\nYou can check out the data [here](https://www.kaggle.com/bryanb/vinbigdata-chestxray-metadata)\nAlso this notebook illustrates how I retrieved these dicom metadata, coming along with an EDA. [EDA notebook](https://www.kaggle.com/bryanb/vinbigdata-chest-x-ray-eda)\n\n## Content\n\ntrain_dicom_metadata.csv : 15000 rows gathering metadata from dicom files located in train folder\ntest_dicom_metadata.csv : 3000 rows gathering metadata from dicom files located in test folder\n\nHope this can help 😉\n"
  }
}