{
  "id": 31583,
  "title": "docker from 0.465 team",
  "content": "<p>Congratulations to the winners and everyone!  </p>\n\n<p>Here's a solution from the 0.465 team, packaged in nviddia-docker image which can be directly used to make predictions.</p>\n\n<p><a href=\"https://github.com/aaalgo/plumo\">https://github.com/aaalgo/plumo</a></p>\n\n<p>Sample output package of one typical case from the SPIE dataset (100MB), for interactive viewing with a browser.</p>\n\n<p><a href=\"http://aaalgo.com/plumo/plumo-example.zip\">http://aaalgo.com/plumo/plumo-example.zip</a></p>\n\n<p><img src=\"http://www.aaalgo.com/plumo/plumo1.png\" alt=\"enter image description here\" title=\"\">\n<img src=\"http://www.aaalgo.com/plumo/plumo2.png\" alt=\"enter image description here\" title=\"\">\n<img src=\"http://www.aaalgo.com/plumo/plumo3.png\" alt=\"enter image description here\" title=\"\"></p>",
  "comments": [
    {
      "id": 175089,
      "author_name": "Uri Merhav",
      "author_url": "",
      "post_date": "2017-04-13T18:27:21.750000",
      "content": "<p>Beautiful stuff, very nice :-)</p>",
      "votes": 1,
      "replies": []
    },
    {
      "id": 178368,
      "author_name": "Wei Dong",
      "author_url": "",
      "post_date": "2017-04-27T12:47:00.273000",
      "content": "<p>@Johnny \"ft model\" is for feature extraction.  The TCIA-LIDC dataset has expert annotation of each nodule.  In addition to the nodule contour, the annotation includes a list of features for each nodule, e.g. subtlety, sphericity, margin, etc.  The \"ft model\" is trained with such annotation and can be used to extract a feature vector from each nodule detected.</p>",
      "votes": 0,
      "replies": []
    },
    {
      "id": 178291,
      "author_name": "Johnny",
      "author_url": "",
      "post_date": "2017-04-27T08:20:30.660000",
      "content": "<p>Great work ! what's meaning of  ft model  ? </p>",
      "votes": 0,
      "replies": []
    },
    {
      "id": 175091,
      "author_name": "Gonçalo Abreu",
      "author_url": "",
      "post_date": "2017-04-13T18:36:09.093000",
      "content": "<p>this is amazing work</p>",
      "votes": 0,
      "replies": []
    }
  ],
  "comment_count": 4,
  "messages": [
    {
      "id": 175066,
      "postDate": "2017-04-13T16:52:02.287Z",
      "content": "<p>Congratulations to the winners and everyone!  </p>\n\n<p>Here's a solution from the 0.465 team, packaged in nviddia-docker image which can be directly used to make predictions.</p>\n\n<p><a href=\"https://github.com/aaalgo/plumo\">https://github.com/aaalgo/plumo</a></p>\n\n<p>Sample output package of one typical case from the SPIE dataset (100MB), for interactive viewing with a browser.</p>\n\n<p><a href=\"http://aaalgo.com/plumo/plumo-example.zip\">http://aaalgo.com/plumo/plumo-example.zip</a></p>\n\n<p><img src=\"http://www.aaalgo.com/plumo/plumo1.png\" alt=\"enter image description here\" title=\"\">\n<img src=\"http://www.aaalgo.com/plumo/plumo2.png\" alt=\"enter image description here\" title=\"\">\n<img src=\"http://www.aaalgo.com/plumo/plumo3.png\" alt=\"enter image description here\" title=\"\"></p>",
      "rawMarkdown": "Congratulations to the winners and everyone!  \n\nHere's a solution from the 0.465 team, packaged in nviddia-docker image which can be directly used to make predictions.\n  \nhttps://github.com/aaalgo/plumo\n\nSample output package of one typical case from the SPIE dataset (100MB), for interactive viewing with a browser.\n\nhttp://aaalgo.com/plumo/plumo-example.zip\n\n![enter image description here][1]\n![enter image description here][2]\n![enter image description here][3]\n\n\n  [1]: http://www.aaalgo.com/plumo/plumo1.png\n  [2]: http://www.aaalgo.com/plumo/plumo2.png\n  [3]: http://www.aaalgo.com/plumo/plumo3.png",
      "votes": 22
    },
    {
      "id": 175089,
      "postDate": "2017-04-13T18:27:21.750Z",
      "content": "<p>Beautiful stuff, very nice :-)</p>",
      "rawMarkdown": "Beautiful stuff, very nice :-)",
      "votes": 1
    },
    {
      "id": 178368,
      "postDate": "2017-04-27T12:47:00.273Z",
      "content": "<p>@Johnny \"ft model\" is for feature extraction.  The TCIA-LIDC dataset has expert annotation of each nodule.  In addition to the nodule contour, the annotation includes a list of features for each nodule, e.g. subtlety, sphericity, margin, etc.  The \"ft model\" is trained with such annotation and can be used to extract a feature vector from each nodule detected.</p>",
      "rawMarkdown": "@Johnny \"ft model\" is for feature extraction.  The TCIA-LIDC dataset has expert annotation of each nodule.  In addition to the nodule contour, the annotation includes a list of features for each nodule, e.g. subtlety, sphericity, margin, etc.  The \"ft model\" is trained with such annotation and can be used to extract a feature vector from each nodule detected."
    },
    {
      "id": 178291,
      "postDate": "2017-04-27T08:20:30.660Z",
      "content": "<p>Great work ! what's meaning of  ft model  ? </p>",
      "rawMarkdown": "Great work ! what's meaning of  ft model  ? "
    },
    {
      "id": 175091,
      "postDate": "2017-04-13T18:36:09.093Z",
      "content": "<p>this is amazing work</p>",
      "rawMarkdown": "this is amazing work"
    }
  ],
  "complete": true,
  "source": "live",
  "fetched_at": "2026-10-24T13:32:11Z",
  "competition_id": 6004,
  "competition_slug": "data-science-bowl-2017",
  "team_id": 487802,
  "solution_url": "/competitions/data-science-bowl-2017/writeups/wei-dong-yuanfang-guan-docker-from-0-465-team"
}