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Models/FR-UNet

FR-UNet

Reported on 14 benchmarks across 2 tasks

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

Medical14 results

  • Medical Image SegmentationonCHASE_DB1
    AUC
    0.9913
    best: 0.9937 (FSG-Net)
  • Medical Image SegmentationonCHASE_DB1
    F1 score
    0.8151
    best: 0.8957 (RV-GAN)
  • Medical Image SegmentationonCHASE_DB1
    Sensitivity
    0.8798
  • Medical Image SegmentationonDRIVE
    AUC
    0.9889
    best: 0.9931 (Swin-Res-Net)
  • Medical Image SegmentationonDRIVE
    Accuracy
    0.9705
    best: 0.9712 (U-Net)
  • Medical Image SegmentationonDRIVE
    F1 score
    0.8316
    best: 0.8322 (FSG-Net)
  • Medical Image SegmentationonDRIVE
    sensitivity
    0.8356
    best: 0.842 (FSG-Net)
  • Retinal Vessel SegmentationonCHASE_DB1
    AUC
    0.9913
    best: 0.9937 (FSG-Net)
  • Retinal Vessel SegmentationonCHASE_DB1
    F1 score
    0.8151
    best: 0.8957 (RV-GAN)
  • Retinal Vessel SegmentationonCHASE_DB1
    Sensitivity
    0.8798
  • Retinal Vessel SegmentationonDRIVE
    AUC
    0.9889
    best: 0.9931 (Swin-Res-Net)
  • Retinal Vessel SegmentationonDRIVE
    Accuracy
    0.9705
    best: 0.9712 (U-Net)
  • Retinal Vessel SegmentationonDRIVE
    F1 score
    0.8316
    best: 0.8322 (FSG-Net)
  • Retinal Vessel SegmentationonDRIVE
    sensitivity
    0.8356
    best: 0.842 (FSG-Net)