{
  "fetched_at": "2026-10-24T13:34:19Z",
  "source_url": "https://www.kaggle.com/competitions/airways/data",
  "pages": [
    {
      "name": "rules",
      "content": "This is a page where you can include rules that participants must accept before joining. You may wish to include rules like:\n\n - Don't cheat!\n - Apply yourself!\n - Have fun!",
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      "name": "data-description",
      "content": "# To be updated \n\n<p>This page shows up underneath the data files. Here you describe what files you have provided and the format of each. There is no single format for this page that is appropriate for all competitons, but you should strive to describe as much as you can here. A little time spent describing the data here can save a lot of time answering questions later.</p>\n<p>This page shows up underneath the data files. Here you describe what files you have provided and the format of each. There is no single format for this page that is appropriate for all competitons, but you should strive to describe as much as you can here. A little time spent describing the data here can save a lot of time answering questions later.</p>\n<p>Participants should be able to answer questions like these after reading the data description: What files do I need? What should I expect the data format to be? What am I predicting? What acroynms will I encounter?</p>\n<h2>File descriptions</h2>\n<ul>\n<li><strong>train.csv</strong> - the training set</li>\n<li><strong>test.csv</strong> - the test set</li>\n<li><strong>sampleSubmission.csv</strong> - a sample submission file in the correct format</li>\n<li><strong>metaData.csv</strong> - supplemental information about the data</li>\n<li>etc...</li>\n</ul>\n<h2>Data fields</h2>\n<ul>\n<li><strong>customer_id</strong> - an anonymous id unique to a given customer</li>\n<li><strong>movie_id</strong> - the id of a movie</li>\n<li><strong>movie_title</strong> - the title of a movie</li>\n<li>etc...</li>\n</ul>\n",
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    {
      "name": "Summary",
      "content": "## **AIRWAYS 2020 (Airway Segmentation 2020)**\n\nThe segmentation of the airway trees in chest computed tomography (CT) images is critical for quantitative assessment of airway diseases including bronchiectasis and chronic obstructive pulmonary disease (COPD). However, obtaining an accurate segmentation of airways from CT scans is difficult due to the high complexity and artifacts of airway structures including complex bifurcations, tapering airways, partial volume effect andmotion artifacts. Fully automated airway segmentation still remains a highchallenging task. Recently, a semantic segmentation using deep convolutional neural networks (CNNs) on airway tree segmentation in CT came true [1], but various kinds of robust airway segmentation methods are still a challenge. We developed a special software to semi-automatically label and correct airway tree as exact as possible [1]. We labeled 100 airway trees of CT from normal to various severities of COPD patients (GOLD I to IV) to build the gold standards, which was confirmed by an expert thoracic radiologist with more than 20 years’ experience. We split 100 datasets into training and validation, test phase I, and test phaseII as 50, 20, and 30. With Kaggle competition platform, we want to open an AIRWAYS challenge in MICCAI 2020 to develop robust airway tree segmentation.\n\n## Reference\n[1] Yun, Jihye, et al. \"Improvement of fully automated airway segmentation on volumetric computed tomographic images using a 2.5 dimensional convolutional neural net.\" Medical image analysis 51 (2019): 13-20.",
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    {
      "name": "Timeline",
      "content": "To be announced",
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    },
    {
      "name": "Prizes",
      "content": "1st : $10000\n2nd – 5th: total $10000 Worth of GPUs\n\nWe’ll prepare the total $10,000 worth of GPUs for the 2nd-5th teams. It can be a $4,000 worth of GPU for 2nd team and $2,000 worth of GPUs for3rd-5th teams.",
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    {
      "name": "Organization",
      "content": "## Organizing team\nNamkug Kim (namkugkim@gmail.com), Asan Medical Center\nJihye Yun (dool0120@gmail.com), Asan Medical Center\nSungwon Ham (swlove920@gmail.com), Asan Medical Center\nKeewon Shin (kevinkwshin@gmail.com), Asan Medical Center\n\n## Contact Person\nJihye Yun (dool0120@gmail.com).",
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    {
      "name": "Evaluation",
      "content": "To be announced",
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