{
  "id": 162782,
  "title": "WSI preprocessing tutorial",
  "url": "/competitions/prostate-cancer-grade-assessment/discussion/162782",
  "author_name": "ilovescience",
  "post_date": "2020-06-30T03:21:11.899000",
  "votes": 8,
  "comment_count": 1,
  "views": 0,
  "content": "<p>I found this 4-part tutorial on processing WSIs for inputting into deep learning pipelines. Thought it might be helpful to participants of this competition:</p>\n\n<p><a href=\"https://developer.ibm.com/articles/an-automatic-method-to-identify-tissues-from-big-whole-slide-images-pt1/\">https://developer.ibm.com/articles/an-automatic-method-to-identify-tissues-from-big-whole-slide-images-pt1/</a></p>",
  "messages": [
    {
      "id": 907566,
      "postDate": "2020-06-30T03:21:11.900Z",
      "content": "<p>I found this 4-part tutorial on processing WSIs for inputting into deep learning pipelines. Thought it might be helpful to participants of this competition:</p>\n\n<p><a href=\"https://developer.ibm.com/articles/an-automatic-method-to-identify-tissues-from-big-whole-slide-images-pt1/\">https://developer.ibm.com/articles/an-automatic-method-to-identify-tissues-from-big-whole-slide-images-pt1/</a></p>",
      "rawMarkdown": "I found this 4-part tutorial on processing WSIs for inputting into deep learning pipelines. Thought it might be helpful to participants of this competition:\n\nhttps://developer.ibm.com/articles/an-automatic-method-to-identify-tissues-from-big-whole-slide-images-pt1/",
      "votes": 8
    },
    {
      "id": 913856,
      "postDate": "2020-07-03T13:31:22.437Z",
      "content": "<p>i looked at it in detail. This is more towards analyzing tissue percentage in sequentially extracted patches ,but not focusing much on how to narrow down precisely on to extraction of only tissue part &amp;  not enclosing any unwanted backgrounds . Moreover biggest challenge to this competition remains handling the noisy data. </p>",
      "rawMarkdown": "i looked at it in detail. This is more towards analyzing tissue percentage in sequentially extracted patches ,but not focusing much on how to narrow down precisely on to extraction of only tissue part &amp;  not enclosing any unwanted backgrounds . Moreover biggest challenge to this competition remains handling the noisy data. \n"
    }
  ],
  "comments": [
    {
      "id": 913856,
      "author_name": "Jaideep",
      "author_url": "",
      "post_date": "2020-07-03T13:31:22.437000",
      "content": "<p>i looked at it in detail. This is more towards analyzing tissue percentage in sequentially extracted patches ,but not focusing much on how to narrow down precisely on to extraction of only tissue part &amp;  not enclosing any unwanted backgrounds . Moreover biggest challenge to this competition remains handling the noisy data. </p>",
      "votes": 0,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "907566": "I found this 4-part tutorial on processing WSIs for inputting into deep learning pipelines. Thought it might be helpful to participants of this competition:\n\nhttps://developer.ibm.com/articles/an-automatic-method-to-identify-tissues-from-big-whole-slide-images-pt1/",
    "913856": "i looked at it in detail. This is more towards analyzing tissue percentage in sequentially extracted patches ,but not focusing much on how to narrow down precisely on to extraction of only tissue part &amp;  not enclosing any unwanted backgrounds . Moreover biggest challenge to this competition remains handling the noisy data. \n"
  }
}