doi.org/10.1007/978-3-319-24574-4_28

Preview meta tags from the doi.org website.

Linked Hostnames

22

Thumbnail

Search Engine Appearance

Google

https://doi.org/10.1007/978-3-319-24574-4_28

U-Net: Convolutional Networks for Biomedical Image Segmentation

There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples...



Bing

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://doi.org/10.1007/978-3-319-24574-4_28

There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples...



DuckDuckGo

https://doi.org/10.1007/978-3-319-24574-4_28

U-Net: Convolutional Networks for Biomedical Image Segmentation

There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples...

  • General Meta Tags

    37
    • title
      U-Net: Convolutional Networks for Biomedical Image Segmentation | SpringerLink
    • charset
      UTF-8
    • X-UA-Compatible
      IE=edge
    • viewport
      width=device-width, initial-scale=1
    • applicable-device
      pc,mobile

  • Open Graph Meta Tags

    6
    • og:url
      https://link.springer.com/chapter/10.1007/978-3-319-24574-4_28
    • og:type
      Paper
    • og:site_name
      SpringerLink
    • og:title
      U-Net: Convolutional Networks for Biomedical Image Segmentation
    • og:description
      There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples...

  • Twitter Meta Tags

    6
    • twitter:site
      SpringerLink
    • twitter:card
      summary
    • twitter:image:alt
      Content cover image
    • twitter:title
      U-Net: Convolutional Networks for Biomedical Image Segmentation
    • twitter:description
      There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples...

  • Item Prop Meta Tags

    3
    • position
      1
    • position
      2
    • position
      3

  • Link Tags

    9
    • apple-touch-icon
      /oscar-static/img/favicons/darwin/apple-touch-icon-6ef0829b9c.png
    • canonical
      https://link.springer.com/chapter/10.1007/978-3-319-24574-4_28
    • icon
      /oscar-static/img/favicons/darwin/android-chrome-192x192.png
    • icon
      /oscar-static/img/favicons/darwin/favicon-32x32.png
    • icon
      /oscar-static/img/favicons/darwin/favicon-16x16.png

Emails

1

Links

76