{
  "id": 161792,
  "title": "Best Research Papers for Competition",
  "url": "/competitions/siim-isic-melanoma-classification/discussion/161792",
  "author_name": "",
  "post_date": "2020-06-26T07:13:32.251974800Z",
  "votes": 13,
  "comment_count": 2,
  "views": 0,
  "content": "<ol>\n<li><a href=\"https://repositorio.unesp.br/bitstream/handle/11449/163796/WOS000424058500001.pdf?sequence=1\">Computational methods for pigmented skin lesion classification in images: review and future trends</a></li>\n<li><a href=\"https://ieeexplore.ieee.org/abstract/document/7762919\">Melanoma Classification on Dermoscopy Images Using a Neural Network Ensemble Model</a></li>\n<li><a href=\"https://ieeexplore.ieee.org/abstract/document/7792699\">Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks</a></li>\n<li><a href=\"https://scholarsmine.mst.edu/cgi/viewcontent.cgi?article=1156&amp;context=comsci_facwork\">Neural Network Diagnosis of Malignant Melanoma from Color Images </a></li>\n<li><a href=\"https://www.sciencedirect.com/science/article/pii/S0959804919301443\">A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task</a></li>\n<li><a href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3191539/\">Colour histogram analysis for melanoma discrimination in clinical images</a></li>\n</ol>",
  "messages": [
    {
      "id": "902479",
      "postDate": "06/26/2020 07:13:32",
      "content": "<ol>\n<li><a href=\"https://repositorio.unesp.br/bitstream/handle/11449/163796/WOS000424058500001.pdf?sequence=1\">Computational methods for pigmented skin lesion classification in images: review and future trends</a></li>\n<li><a href=\"https://ieeexplore.ieee.org/abstract/document/7762919\">Melanoma Classification on Dermoscopy Images Using a Neural Network Ensemble Model</a></li>\n<li><a href=\"https://ieeexplore.ieee.org/abstract/document/7792699\">Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks</a></li>\n<li><a href=\"https://scholarsmine.mst.edu/cgi/viewcontent.cgi?article=1156&amp;context=comsci_facwork\">Neural Network Diagnosis of Malignant Melanoma from Color Images </a></li>\n<li><a href=\"https://www.sciencedirect.com/science/article/pii/S0959804919301443\">A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task</a></li>\n<li><a href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3191539/\">Colour histogram analysis for melanoma discrimination in clinical images</a></li>\n</ol>",
      "rawMarkdown": "1. [Computational methods for pigmented skin lesion classification in images: review and future trends](https://repositorio.unesp.br/bitstream/handle/11449/163796/WOS000424058500001.pdf?sequence=1)\n2. [Melanoma Classification on Dermoscopy Images Using a Neural Network Ensemble Model](https://ieeexplore.ieee.org/abstract/document/7762919)\n3. [Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks](https://ieeexplore.ieee.org/abstract/document/7792699)\n4. [Neural Network Diagnosis of Malignant Melanoma from Color Images ](https://scholarsmine.mst.edu/cgi/viewcontent.cgi?article=1156&amp;context=comsci_facwork)\n5. [A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task](https://www.sciencedirect.com/science/article/pii/S0959804919301443)\n6. [Colour histogram analysis for melanoma discrimination in clinical images](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3191539/)",
      "votes": null
    },
    {
      "id": "903955",
      "postDate": "06/27/2020 08:29:12",
      "content": "<p>Thanks</p>",
      "rawMarkdown": "Thanks",
      "votes": null
    },
    {
      "id": "903956",
      "postDate": "06/27/2020 08:31:24",
      "content": "<p>Welcome!</p>",
      "rawMarkdown": "Welcome!",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 903955,
      "author_name": "benboren",
      "author_url": "",
      "post_date": "06/27/2020 08:29:12",
      "content": "<p>Thanks</p>",
      "votes": null,
      "replies": [
        {
          "id": 903956,
          "author_name": "ishandutta",
          "author_url": "",
          "post_date": "06/27/2020 08:31:24",
          "content": "<p>Welcome!</p>",
          "votes": null,
          "replies": []
        }
      ]
    }
  ],
  "raw_markdown_by_id": {
    "902479": "1. [Computational methods for pigmented skin lesion classification in images: review and future trends](https://repositorio.unesp.br/bitstream/handle/11449/163796/WOS000424058500001.pdf?sequence=1)\n2. [Melanoma Classification on Dermoscopy Images Using a Neural Network Ensemble Model](https://ieeexplore.ieee.org/abstract/document/7762919)\n3. [Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks](https://ieeexplore.ieee.org/abstract/document/7792699)\n4. [Neural Network Diagnosis of Malignant Melanoma from Color Images ](https://scholarsmine.mst.edu/cgi/viewcontent.cgi?article=1156&amp;context=comsci_facwork)\n5. [A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task](https://www.sciencedirect.com/science/article/pii/S0959804919301443)\n6. [Colour histogram analysis for melanoma discrimination in clinical images](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3191539/)",
    "903955": "Thanks",
    "903956": "Welcome!"
  },
  "source": "meta"
}