{
  "id": 358406,
  "title": "Educational Merit Distinction",
  "url": "/competitions/rsna-2022-cervical-spine-fracture-detection/discussion/358406",
  "author_name": "John Mongan",
  "post_date": "2022-10-07T17:48:49.236000",
  "votes": 22,
  "comment_count": 1,
  "views": 0,
  "content": "<p>The competition hosts are pleased to announce an additional aspect of this year's challenge: The Educational Merit Distinction.</p>\n<p>The top ten placing entrants on the private leader board at the close of the competition will be reviewed by a panel of judges for the purpose of awarding an Educational Merit distinction. Judges will evaluate the training code, inference code and associated documentation and supporting materials. </p>\n<p>Judges will be looking for:</p>\n<ul>\n<li>clarity and comprehensiveness of the explanation of the approach taken by the entrant</li>\n<li>clarity, accessibility, organization and architecture of the code</li>\n<li>estimated effort required to run the code (e.g. reproduce results by retraining, run inference on a new set of data)</li>\n<li>ease of re-use and re-purposing of code</li>\n<li>novelty of approach</li>\n</ul>\n<p>Entrants awarded the Educational Merit distinction will be invited to work with the competition hosts to prepare and publish a manuscript providing an education-oriented overview of the awardee’s entry and choices made in creating it.  This distinction is judged and administered solely by the hosts and does not involve kaggle. Only the top 10 placing entrants (on the private leaderboard) at the close of the competition are eligible for this distinction. There is no additional monetary prize associated with this distinction.</p>\n<p>John Mongan<br>\n(on behalf of the RSNA Machine Learning Steering Subcommittee) </p>",
  "messages": [
    {
      "id": 1977015,
      "postDate": "2022-10-07T17:48:49.237Z",
      "content": "<p>The competition hosts are pleased to announce an additional aspect of this year's challenge: The Educational Merit Distinction.</p>\n<p>The top ten placing entrants on the private leader board at the close of the competition will be reviewed by a panel of judges for the purpose of awarding an Educational Merit distinction. Judges will evaluate the training code, inference code and associated documentation and supporting materials. </p>\n<p>Judges will be looking for:</p>\n<ul>\n<li>clarity and comprehensiveness of the explanation of the approach taken by the entrant</li>\n<li>clarity, accessibility, organization and architecture of the code</li>\n<li>estimated effort required to run the code (e.g. reproduce results by retraining, run inference on a new set of data)</li>\n<li>ease of re-use and re-purposing of code</li>\n<li>novelty of approach</li>\n</ul>\n<p>Entrants awarded the Educational Merit distinction will be invited to work with the competition hosts to prepare and publish a manuscript providing an education-oriented overview of the awardee’s entry and choices made in creating it.  This distinction is judged and administered solely by the hosts and does not involve kaggle. Only the top 10 placing entrants (on the private leaderboard) at the close of the competition are eligible for this distinction. There is no additional monetary prize associated with this distinction.</p>\n<p>John Mongan<br>\n(on behalf of the RSNA Machine Learning Steering Subcommittee) </p>",
      "rawMarkdown": "The competition hosts are pleased to announce an additional aspect of this year's challenge: The Educational Merit Distinction.\n\nThe top ten placing entrants on the private leader board at the close of the competition will be reviewed by a panel of judges for the purpose of awarding an Educational Merit distinction. Judges will evaluate the training code, inference code and associated documentation and supporting materials. \n\nJudges will be looking for:\n\n- clarity and comprehensiveness of the explanation of the approach taken by the entrant\n- clarity, accessibility, organization and architecture of the code\n- estimated effort required to run the code (e.g. reproduce results by retraining, run inference on a new set of data)\n- ease of re-use and re-purposing of code\n- novelty of approach\n\nEntrants awarded the Educational Merit distinction will be invited to work with the competition hosts to prepare and publish a manuscript providing an education-oriented overview of the awardee’s entry and choices made in creating it.  This distinction is judged and administered solely by the hosts and does not involve kaggle. Only the top 10 placing entrants (on the private leaderboard) at the close of the competition are eligible for this distinction. There is no additional monetary prize associated with this distinction.\n\nJohn Mongan\n(on behalf of the RSNA Machine Learning Steering Subcommittee) \n",
      "votes": 22
    },
    {
      "id": 2001111,
      "postDate": "2022-10-23T19:07:15.827Z",
      "content": "<p>go RSNA !🤘🤘</p>",
      "rawMarkdown": "go RSNA !🤘🤘",
      "votes": 1
    }
  ],
  "comments": [
    {
      "id": 2001111,
      "author_name": "GUNER",
      "author_url": "",
      "post_date": "2022-10-23T19:07:15.827000",
      "content": "<p>go RSNA !🤘🤘</p>",
      "votes": 1,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "1977015": "The competition hosts are pleased to announce an additional aspect of this year's challenge: The Educational Merit Distinction.\n\nThe top ten placing entrants on the private leader board at the close of the competition will be reviewed by a panel of judges for the purpose of awarding an Educational Merit distinction. Judges will evaluate the training code, inference code and associated documentation and supporting materials. \n\nJudges will be looking for:\n\n- clarity and comprehensiveness of the explanation of the approach taken by the entrant\n- clarity, accessibility, organization and architecture of the code\n- estimated effort required to run the code (e.g. reproduce results by retraining, run inference on a new set of data)\n- ease of re-use and re-purposing of code\n- novelty of approach\n\nEntrants awarded the Educational Merit distinction will be invited to work with the competition hosts to prepare and publish a manuscript providing an education-oriented overview of the awardee’s entry and choices made in creating it.  This distinction is judged and administered solely by the hosts and does not involve kaggle. Only the top 10 placing entrants (on the private leaderboard) at the close of the competition are eligible for this distinction. There is no additional monetary prize associated with this distinction.\n\nJohn Mongan\n(on behalf of the RSNA Machine Learning Steering Subcommittee) \n",
    "2001111": "go RSNA !🤘🤘"
  }
}