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16 та иш

Иш: Brain tumor segmentation capabilities of 3D deep learning architectures (U-Net, V-Net, Attention U-Net, ResNet-based U-Net, Transformer-based model)

  1. Deep Residual Learning for Image Recognition

    Kaiming He, Xiangyu Zhang, Shaoqing Ren +1

    Мақола201661 иқтибос
    ABI
  2. U-Net: Convolutional Networks for Biomedical Image Segmentation

    Olaf Ronneberger, Philipp Fischer, Thomas Brox

    Боб201532 иқтибос
    ABI
  3. A survey on Image Data Augmentation for Deep Learning

    Connor Shorten, Taghi M. Khoshgoftaar

    Мақола201910 иқтибос
    ABI
  4. Сарлавҳасиз

    Бошқа9 иқтибос
    ABI
  5. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

    Bjoern Menze, András Jakab, Stefan Bauer +65

    Шарҳ мақола20146 иқтибос
    ABI
  6. UNETR: Transformers for 3D Medical Image Segmentation

    Ali Hatamizadeh, Yucheng Tang, Vishwesh Nath +5

    Мақола20226 иқтибос
    ABI
  7. Pattern Recognition and Machine Learning

    Китоб20064 иқтибос
    ABI
  8. Learning Spatiotemporal Features with 3D Convolutional Networks

    Du Tran, Lubomir Bourdev, Rob Fergus +2

    Препринт20152 иқтибос
    ABI
  9. Current Methods in Medical Image Segmentation

    Dzung L. Pham, Chenyang Xu, Jerry L. Prince

    Шарҳ мақола20002 иқтибос
    ABI
  10. Comparing images using the Hausdorff distance

    D.P. Huttenlocher, G.A. Klanderman, W.J. Rucklidge

    Мақола19932 иқтибос
    ABI
  11. Сарлавҳасиз

    Бошқа1 иқтибос
    ABI
  12. Сарлавҳасиз

    Бошқа1 иқтибос
    ABI
  13. Сарлавҳасиз

    Бошқа1 иқтибос
    ABI
  14. Сарлавҳасиз

    Бошқа1 иқтибос
    ABI