{
  "id": 54577,
  "title": "Concise List of Papers For Nuclei Segmentation",
  "url": "/competitions/data-science-bowl-2018/discussion/54577",
  "author_name": "",
  "post_date": "2018-04-15T08:03:58.358428800Z",
  "votes": 7,
  "comment_count": 3,
  "views": 0,
  "content": "<p>Here are the concise list of papers that are mostly tried and mentioned in discussions. \nSpecial thanks to <a href=\"https://www.kaggle.com/hengck23/\">HengCherKeng</a>, <a href=\"https://www.kaggle.com/iglovikov\">Vladimir</a></p>\n\n<h2>Worth Mentioning some of this papers are for 3D images for 2D images modification required in the original papers.</h2>\n\n<ol>\n<li><a href=\"https://arxiv.org/abs/1505.04597\">UNet</a></li>\n<li><a href=\"https://arxiv.org/abs/1709.07330\">H-DenseUNet: Hybrid Densely Connected UNet for Liver and Liver\nTumor Segmentation from CT Volumes</a></li>\n<li><a href=\"https://arxiv.org/pdf/1804.04241.pdf\">Capsules for Object Segmentation</a></li>\n<li><a href=\"https://arxiv.org/abs/1611.09326\">The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for\nSemantic Segmentation</a></li>\n<li><a href=\"https://arxiv.org/abs/1801.05746\">TernausNet</a></li>\n<li><a href=\"https://arxiv.org/abs/1311.2524\">RCNN</a></li>\n<li><a href=\"https://arxiv.org/abs/1504.08083\">Fast - RCNN</a></li>\n<li><a href=\"https://arxiv.org/pdf/1506.01497.pdf\">Faster - RCNN</a></li>\n<li><a href=\"https://arxiv.org/abs/1703.06870\">Mask-RCNN</a></li>\n<li><a href=\"https://arxiv.org/abs/1702.05970\">Automatic Liver and Tumor Segmentation of CT and MRI Volumes using\n Cascaded Fully Convolutional Neural Networks</a></li>\n<li><a href=\"https://arxiv.org/pdf/1710.09288.pdf\">ADVERSARIAL DEEP STRUCTURED NETS FOR MASS SEGMENTATION FROM\n MAMMOGRAMS</a></li>\n<li><a href=\"https://arxiv.org/abs/1702.05970\">Automatic Liver and Tumor Segmentation of CT and MRI Volumes using\n Cascaded Fully Convolutional Neural Networks</a></li>\n<li><a href=\"https://arxiv.org/abs/1708.02002\">RetinaNet</a></li>\n<li><a href=\"https://arxiv.org/abs/1803.02786\">A Deep Learning Algorithm for One-step Contour Aware Nuclei\n Segmentation of Histopathological Images</a></li>\n<li><a href=\"https://arxiv.org/abs/1612.03144\">Feature Pyramid Networks for Object Detection</a></li>\n</ol>",
  "messages": [
    {
      "id": "314312",
      "postDate": "04/15/2018 08:03:58",
      "content": "<p>Here are the concise list of papers that are mostly tried and mentioned in discussions. \nSpecial thanks to <a href=\"https://www.kaggle.com/hengck23/\">HengCherKeng</a>, <a href=\"https://www.kaggle.com/iglovikov\">Vladimir</a></p>\n\n<h2>Worth Mentioning some of this papers are for 3D images for 2D images modification required in the original papers.</h2>\n\n<ol>\n<li><a href=\"https://arxiv.org/abs/1505.04597\">UNet</a></li>\n<li><a href=\"https://arxiv.org/abs/1709.07330\">H-DenseUNet: Hybrid Densely Connected UNet for Liver and Liver\nTumor Segmentation from CT Volumes</a></li>\n<li><a href=\"https://arxiv.org/pdf/1804.04241.pdf\">Capsules for Object Segmentation</a></li>\n<li><a href=\"https://arxiv.org/abs/1611.09326\">The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for\nSemantic Segmentation</a></li>\n<li><a href=\"https://arxiv.org/abs/1801.05746\">TernausNet</a></li>\n<li><a href=\"https://arxiv.org/abs/1311.2524\">RCNN</a></li>\n<li><a href=\"https://arxiv.org/abs/1504.08083\">Fast - RCNN</a></li>\n<li><a href=\"https://arxiv.org/pdf/1506.01497.pdf\">Faster - RCNN</a></li>\n<li><a href=\"https://arxiv.org/abs/1703.06870\">Mask-RCNN</a></li>\n<li><a href=\"https://arxiv.org/abs/1702.05970\">Automatic Liver and Tumor Segmentation of CT and MRI Volumes using\n Cascaded Fully Convolutional Neural Networks</a></li>\n<li><a href=\"https://arxiv.org/pdf/1710.09288.pdf\">ADVERSARIAL DEEP STRUCTURED NETS FOR MASS SEGMENTATION FROM\n MAMMOGRAMS</a></li>\n<li><a href=\"https://arxiv.org/abs/1702.05970\">Automatic Liver and Tumor Segmentation of CT and MRI Volumes using\n Cascaded Fully Convolutional Neural Networks</a></li>\n<li><a href=\"https://arxiv.org/abs/1708.02002\">RetinaNet</a></li>\n<li><a href=\"https://arxiv.org/abs/1803.02786\">A Deep Learning Algorithm for One-step Contour Aware Nuclei\n Segmentation of Histopathological Images</a></li>\n<li><a href=\"https://arxiv.org/abs/1612.03144\">Feature Pyramid Networks for Object Detection</a></li>\n</ol>",
      "rawMarkdown": "Here are the concise list of papers that are mostly tried and mentioned in discussions. \nSpecial thanks to [HengCherKeng][1], [Vladimir][2]\n\nWorth Mentioning some of this papers are for 3D images for 2D images modification required in the original papers.\n----------\n\n 1. [UNet][3]\n 2. [H-DenseUNet: Hybrid Densely Connected UNet for Liver and Liver\n    Tumor Segmentation from CT Volumes][4]\n 3. [Capsules for Object Segmentation][5]\n 4. [The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for\n    Semantic Segmentation][6]\n 5. [TernausNet][7]\n 6. [RCNN][8]\n 7. [Fast - RCNN][9]\n 8. [Faster - RCNN][10]\n 9. [Mask-RCNN][11]\n 10. [Automatic Liver and Tumor Segmentation of CT and MRI Volumes using\n     Cascaded Fully Convolutional Neural Networks][12]\n 11. [ADVERSARIAL DEEP STRUCTURED NETS FOR MASS SEGMENTATION FROM\n     MAMMOGRAMS][13]\n 12. [Automatic Liver and Tumor Segmentation of CT and MRI Volumes using\n     Cascaded Fully Convolutional Neural Networks][14]\n 13. [RetinaNet][15]\n 14. [A Deep Learning Algorithm for One-step Contour Aware Nuclei\n     Segmentation of Histopathological Images][16]\n 15. [Feature Pyramid Networks for Object Detection][17]\n\n  [1]: https://www.kaggle.com/hengck23/\n  [2]: https://www.kaggle.com/iglovikov\n  [3]: https://arxiv.org/abs/1505.04597\n  [4]: https://arxiv.org/abs/1709.07330\n  [5]: https://arxiv.org/pdf/1804.04241.pdf\n  [6]: https://arxiv.org/abs/1611.09326\n  [7]: https://arxiv.org/abs/1801.05746\n  [8]: https://arxiv.org/abs/1311.2524\n  [9]: https://arxiv.org/abs/1504.08083\n  [10]: https://arxiv.org/pdf/1506.01497.pdf\n  [11]: https://arxiv.org/abs/1703.06870\n  [12]: https://arxiv.org/abs/1702.05970\n  [13]: https://arxiv.org/pdf/1710.09288.pdf\n  [14]: https://arxiv.org/abs/1702.05970\n  [15]: https://arxiv.org/abs/1708.02002\n  [16]: https://arxiv.org/abs/1803.02786\n  [17]: https://arxiv.org/abs/1612.03144",
      "votes": null
    },
    {
      "id": "314328",
      "postDate": "04/15/2018 08:56:43",
      "content": "<p>those are really great articles , thank you for sharing  . </p>",
      "rawMarkdown": "those are really great articles , thank you for sharing  .",
      "votes": null
    },
    {
      "id": "314535",
      "postDate": "04/15/2018 18:58:35",
      "content": "<p>Thanks, Hope it helps.</p>",
      "rawMarkdown": "Thanks, Hope it helps.",
      "votes": null
    },
    {
      "id": "314640",
      "postDate": "04/16/2018 02:32:17",
      "content": "<p>Thank you for sharing!</p>",
      "rawMarkdown": "Thank you for sharing!",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 314328,
      "author_name": "azirtn",
      "author_url": "",
      "post_date": "04/15/2018 08:56:43",
      "content": "<p>those are really great articles , thank you for sharing  . </p>",
      "votes": null,
      "replies": [
        {
          "id": 314535,
          "author_name": "iitrsamrat",
          "author_url": "",
          "post_date": "04/15/2018 18:58:35",
          "content": "<p>Thanks, Hope it helps.</p>",
          "votes": null,
          "replies": []
        }
      ]
    },
    {
      "id": 314640,
      "author_name": "deniall",
      "author_url": "",
      "post_date": "04/16/2018 02:32:17",
      "content": "<p>Thank you for sharing!</p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "314312": "Here are the concise list of papers that are mostly tried and mentioned in discussions. \nSpecial thanks to [HengCherKeng][1], [Vladimir][2]\n\nWorth Mentioning some of this papers are for 3D images for 2D images modification required in the original papers.\n----------\n\n 1. [UNet][3]\n 2. [H-DenseUNet: Hybrid Densely Connected UNet for Liver and Liver\n    Tumor Segmentation from CT Volumes][4]\n 3. [Capsules for Object Segmentation][5]\n 4. [The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for\n    Semantic Segmentation][6]\n 5. [TernausNet][7]\n 6. [RCNN][8]\n 7. [Fast - RCNN][9]\n 8. [Faster - RCNN][10]\n 9. [Mask-RCNN][11]\n 10. [Automatic Liver and Tumor Segmentation of CT and MRI Volumes using\n     Cascaded Fully Convolutional Neural Networks][12]\n 11. [ADVERSARIAL DEEP STRUCTURED NETS FOR MASS SEGMENTATION FROM\n     MAMMOGRAMS][13]\n 12. [Automatic Liver and Tumor Segmentation of CT and MRI Volumes using\n     Cascaded Fully Convolutional Neural Networks][14]\n 13. [RetinaNet][15]\n 14. [A Deep Learning Algorithm for One-step Contour Aware Nuclei\n     Segmentation of Histopathological Images][16]\n 15. [Feature Pyramid Networks for Object Detection][17]\n\n  [1]: https://www.kaggle.com/hengck23/\n  [2]: https://www.kaggle.com/iglovikov\n  [3]: https://arxiv.org/abs/1505.04597\n  [4]: https://arxiv.org/abs/1709.07330\n  [5]: https://arxiv.org/pdf/1804.04241.pdf\n  [6]: https://arxiv.org/abs/1611.09326\n  [7]: https://arxiv.org/abs/1801.05746\n  [8]: https://arxiv.org/abs/1311.2524\n  [9]: https://arxiv.org/abs/1504.08083\n  [10]: https://arxiv.org/pdf/1506.01497.pdf\n  [11]: https://arxiv.org/abs/1703.06870\n  [12]: https://arxiv.org/abs/1702.05970\n  [13]: https://arxiv.org/pdf/1710.09288.pdf\n  [14]: https://arxiv.org/abs/1702.05970\n  [15]: https://arxiv.org/abs/1708.02002\n  [16]: https://arxiv.org/abs/1803.02786\n  [17]: https://arxiv.org/abs/1612.03144",
    "314328": "those are really great articles , thank you for sharing  .",
    "314535": "Thanks, Hope it helps.",
    "314640": "Thank you for sharing!"
  },
  "source": "meta"
}