{
  "id": "1703.03372",
  "version": "v1",
  "published": "2017-03-09T17:52:28.000Z",
  "updated": "2017-03-09T17:52:28.000Z",
  "title": "LesionSeg: Semantic segmentation of skin lesions using Deep Convolutional Neural Network",
  "authors": [
    "Dhanesh Ramachandram",
    "Terrance DeVries"
  ],
  "categories": [
    "cs.CV",
    "cs.AI",
    "cs.NE"
  ],
  "abstract": "We present a method for skin lesion segmentation for the ISIC 2017 Skin Lesion Segmentation Challenge. Our approach is based on a Fully Convolutional Network architecture which is trained end to end, from scratch, on a limited dataset. Our semantic segmentation architecture utilizes several recent innovations in particularly in the combined use of (i) use of \\emph{atrous} convolutions to increase the effective field of view of the network's receptive field without increasing the number of parameters, (ii) the use of network-in-network $1\\times1$ convolution layers to increase network capacity without incereasing the number of parameters and (iii) state-of-art super-resolution upsampling of predictions using subpixel CNN layers for accurate and efficient upsampling of predictions. We achieved a IOU score of 0.642 on the validation set provided by the organisers.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2017-03-09T17:52:28.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "deep convolutional neural network",
      "skin lesion segmentation challenge",
      "semantic segmentation architecture utilizes",
      "fully convolutional network architecture",
      "increase network capacity"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}