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Models/ResUNet++ + TTA + CRF

ResUNet++ + TTA + CRF

Reported on 7 benchmarks across 1 task · 1 paper

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Medical7 results

  • Medical Image SegmentationonKvasir-SEG
    FPS· 2021-07-26
    69.59
    best: 182.38 (ColonSegNet)
    A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time AugmentationarXiv:2107.12435
  • Medical Image SegmentationonKvasir-SEG
    mIoU· 2021-07-26
    0.78
    best: 0.9065 (EffiSegNet-B5)
    A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time AugmentationarXiv:2107.12435
  • Medical Image SegmentationonKvasir-SEG
    mean Dice· 2021-07-26
    0.8508
    best: 0.9502 (DUCK-Net)
    A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time AugmentationarXiv:2107.12435
  • Medical Image SegmentationonCVC-VideoClinicDB
    Dice· 2021-07-26
    0.813
    best: 0.926 (Meta-Polyp)
    A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time AugmentationarXiv:2107.12435
  • Medical Image SegmentationonCVC-VideoClinicDB
    Recall· 2021-07-26
    0.6875
    best: 0.7749 (ResUNet++)
    A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time AugmentationarXiv:2107.12435
  • Medical Image SegmentationonCVC-VideoClinicDB
    mIoU· 2021-07-26
    0.8477
    best: 0.8739 (ResUNet++ + CRF)
    A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time AugmentationarXiv:2107.12435
  • Medical Image SegmentationonCVC-VideoClinicDB
    precision· 2021-07-26
    0.6276
    best: 0.6706 (ResUNet++ + CRF)
    A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time AugmentationarXiv:2107.12435