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NIST · ИТ · fe8331a9e295afb1ffae

fe8331a9e295afb1ffae · Cell Image Segmentation using Generative Adversarial Networks, Transfer Learning, and Augmentations

Cell Image Segmentation using Generative Adversarial Networks, Transfer Learning, and Augmentations

We address the problem of segmenting cell contours from microscopy images of human induced pluripotent Retinal Pigment Epithelial stem cells (iRPE) using Convolutional Neural Networks (CNN). Our goal is to compare the accuracy gains of CNN-based

Официальный источник · 2019-06-17

Полные импортированные сведения карточки
{
  "title_en": "Cell Image Segmentation using Generative Adversarial Networks, Transfer Learning, and Augmentations",
  "designation": "",
  "series": "",
  "status": "",
  "date": "2019-06-17",
  "date_original": "June 17, 2019",
  "url": "https://www.nist.gov/publications/cell-image-segmentation-using-generative-adversarial-networks-transfer-learning-and",
  "source": "NIST Publications",
  "display_en": "",
  "authors_en": "Michael P. Majurski , Petru S. Manescu , Sarala Padi , Nicholas J. Schaub , Nathan A. Hotaling , Carl Simon Jr. , Peter Bajcsy",
  "abstract_excerpt_en": "We address the problem of segmenting cell contours from microscopy images of human induced pluripotent Retinal Pigment Epithelial stem cells (iRPE) using Convolutional Neural Networks (CNN). Our goal is to compare the accuracy gains of CNN-based",
  "id": "fe8331a9e295afb1ffae",
  "sources": [
    "NIST Publications"
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  "source_records": [
    {
      "title_en": "Cell Image Segmentation using Generative Adversarial Networks, Transfer Learning, and Augmentations",
      "designation": "",
      "series": "",
      "status": "",
      "date": "2019-06-17",
      "date_original": "June 17, 2019",
      "url": "https://www.nist.gov/publications/cell-image-segmentation-using-generative-adversarial-networks-transfer-learning-and",
      "source": "NIST Publications",
      "display_en": "",
      "authors_en": "Michael P. Majurski , Petru S. Manescu , Sarala Padi , Nicholas J. Schaub , Nathan A. Hotaling , Carl Simon Jr. , Peter Bajcsy",
      "abstract_excerpt_en": "We address the problem of segmenting cell contours from microscopy images of human induced pluripotent Retinal Pigment Epithelial stem cells (iRPE) using Convolutional Neural Networks (CNN). Our goal is to compare the accuracy gains of CNN-based"
    }
  ],
  "translation_state": "none"
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