{
  "id": 152252,
  "title": "multiple instance learning-based deep learning system",
  "url": "/competitions/prostate-cancer-grade-assessment/discussion/152252",
  "author_name": "zs_mip",
  "post_date": "2020-05-19T03:59:43.539000",
  "votes": 12,
  "comment_count": 0,
  "views": 0,
  "content": "<p><a href=\"https://louris.cn/assets/files/Clinical-grade_computational_pathology_using_weakly_supervised_deep_learning_on_whole_slide_images.pdf\">Clinical-grade computational pathology using weakly supervised deep learning on whole slide images</a> proposed a weakly supervised method to overcome the absence of manual ROI(region of interest) annotations, and provided the PyTorch code <a href=\"https://github.com/MSKCC-Computational-Pathology/MIL-nature-medicine-2019\">here</a>.\n<img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3721963%2F1b74fcc0a17453cb6eb2ed12f97d6d84%2FQQ20200519115646.jpg?generation=1589860645279689&amp;alt=media\" alt=\"\"></p>",
  "messages": [
    {
      "id": 853256,
      "postDate": "2020-05-19T03:59:43.540Z",
      "content": "<p><a href=\"https://louris.cn/assets/files/Clinical-grade_computational_pathology_using_weakly_supervised_deep_learning_on_whole_slide_images.pdf\">Clinical-grade computational pathology using weakly supervised deep learning on whole slide images</a> proposed a weakly supervised method to overcome the absence of manual ROI(region of interest) annotations, and provided the PyTorch code <a href=\"https://github.com/MSKCC-Computational-Pathology/MIL-nature-medicine-2019\">here</a>.\n<img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3721963%2F1b74fcc0a17453cb6eb2ed12f97d6d84%2FQQ20200519115646.jpg?generation=1589860645279689&amp;alt=media\" alt=\"\"></p>",
      "rawMarkdown": "[Clinical-grade computational pathology using weakly supervised deep learning on whole slide images](https://louris.cn/assets/files/Clinical-grade_computational_pathology_using_weakly_supervised_deep_learning_on_whole_slide_images.pdf) proposed a weakly supervised method to overcome the absence of manual ROI(region of interest) annotations, and provided the PyTorch code [here](https://github.com/MSKCC-Computational-Pathology/MIL-nature-medicine-2019).\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3721963%2F1b74fcc0a17453cb6eb2ed12f97d6d84%2FQQ20200519115646.jpg?generation=1589860645279689&amp;alt=media)\n",
      "votes": 12
    }
  ],
  "comments": [],
  "raw_markdown_by_id": {
    "853256": "[Clinical-grade computational pathology using weakly supervised deep learning on whole slide images](https://louris.cn/assets/files/Clinical-grade_computational_pathology_using_weakly_supervised_deep_learning_on_whole_slide_images.pdf) proposed a weakly supervised method to overcome the absence of manual ROI(region of interest) annotations, and provided the PyTorch code [here](https://github.com/MSKCC-Computational-Pathology/MIL-nature-medicine-2019).\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3721963%2F1b74fcc0a17453cb6eb2ed12f97d6d84%2FQQ20200519115646.jpg?generation=1589860645279689&amp;alt=media)\n"
  }
}