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Models/PVT-GCASCADE

PVT-GCASCADE

Reported on 29 benchmarks across 2 tasks · 1 paper · 4 SOTA

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

Medical32 results

  • Medical Image SegmentationonISIC 2018
    DSC· 2023-10-24
    91.51
    best: 92.1 (ProMISe)
    SOTA
    G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image SegmentationarXiv:2310.16175
  • Medical Image SegmentationonISIC 2018
    mIoU· 2023-10-24
    86.53
    SOTA
    G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image SegmentationarXiv:2310.16175
  • Medical Image SegmentationonMICCAI 2015 Multi-Atlas Abdomen Labeling Challenge
    Avg HD· 2023-10-24
    15.83
    best: 20.23 (PVT-CASCADE)
    SOTA
    G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image SegmentationarXiv:2310.16175
  • Medical Image SegmentationonDRIVE
    Recall· 2023-10-24
    0.83
    SOTA
    G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image SegmentationarXiv:2310.16175
  • Medical Image SegmentationonKvasir-SEG
    mIoU· 2023-10-24
    0.879
    best: 0.9065 (EffiSegNet-B5)
    G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image SegmentationarXiv:2310.16175
  • Medical Image SegmentationonKvasir-SEG
    mean Dice· 2023-10-24
    0.9274
    best: 0.9502 (DUCK-Net)
    G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image SegmentationarXiv:2310.16175
  • Medical Image SegmentationonCVC-ColonDB
    mIoU· 2023-10-24
    0.746
    best: 0.9096 (RAPUNet)
    G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image SegmentationarXiv:2310.16175
  • Medical Image SegmentationonCVC-ColonDB
    mean Dice· 2023-10-24
    0.8261
    best: 0.9526 (RAPUNet)
    G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image SegmentationarXiv:2310.16175
  • Medical Image SegmentationonMICCAI 2015 Multi-Atlas Abdomen Labeling Challenge
    Avg DSC· 2023-10-24
    83.28
    best: 84.9 (MERIT)
    G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image SegmentationarXiv:2310.16175
  • Medical Image SegmentationonAutomatic Cardiac Diagnosis Challenge (ACDC)
    Avg DSC· 2023-10-24
    91.95
    best: 94.26 (FCT)
    G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image SegmentationarXiv:2310.16175
  • Medical Image SegmentationonCVC-ClinicDB
    mIoU· 2023-10-24
    0.9018
    best: 0.9343 (DUCK-Net)
    G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image SegmentationarXiv:2310.16175
  • Medical Image SegmentationonCVC-ClinicDB
    mean Dice· 2023-10-24
    0.9468
    best: 0.9684 (DUCK-Net)
    G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image SegmentationarXiv:2310.16175
  • Medical Image SegmentationonCHASE_DB1
    DSC· 2023-10-24
    0.8251
    best: 0.8267 (MERIT-GCASCADE)
    G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image SegmentationarXiv:2310.16175
  • Medical Image SegmentationonDRIVE
    F1 score· 2023-10-24
    0.821
    best: 0.8322 (FSG-Net)
    G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image SegmentationarXiv:2310.16175
  • Medical Image SegmentationonDRIVE
    Specificity· 2023-10-24
    0.9822
    best: 0.9844 (MERIT-GCASCADE)
    G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image SegmentationarXiv:2310.16175
  • Medical Image SegmentationonDRIVE
    mIoU· 2023-10-24
    0.697
    best: 0.8406 (FSG-Net)
    G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image SegmentationarXiv:2310.16175
  • Medical Image SegmentationonCHASE_DB1
    F1 score· 2023-10-24
    0.8251
    best: 0.8957 (RV-GAN)
    G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image SegmentationarXiv:2310.16175
  • Medical Image SegmentationonCHASE_DB1
    Sensitivity· 2023-10-24
    0.8584
    best: 0.8798 (FR-UNet)
    G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image SegmentationarXiv:2310.16175
  • Medical Image SegmentationonCHASE_DB1
    mIOU· 2023-10-24
    0.7024
    best: 0.9705 (RV-GAN)
    G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image SegmentationarXiv:2310.16175
  • Medical Image SegmentationonDRIVE
    Accuracy· 2023-10-24
    0.9689
    best: 0.9712 (U-Net)
    G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image SegmentationarXiv:2310.16175
  • Medical Image SegmentationonDRIVE
    F1 score· 2023-10-24
    0.821
    best: 0.8322 (FSG-Net)
    G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image SegmentationarXiv:2310.16175
  • Medical Image SegmentationonDRIVE
    Specificity· 2023-10-24
    0.9822
    best: 0.9844 (MERIT-GCASCADE)
    G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image SegmentationarXiv:2310.16175
  • Medical Image SegmentationonDRIVE
    mIoU· 2023-10-24
    0.697
    best: 0.8406 (FSG-Net)
    G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image SegmentationarXiv:2310.16175
  • Medical Image SegmentationonDRIVE
    sensitivity· 2023-10-24
    0.83
    best: 0.842 (FSG-Net)
    G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image SegmentationarXiv:2310.16175
  • Retinal Vessel SegmentationonCHASE_DB1
    F1 score· 2023-10-24
    0.8251
    best: 0.8957 (RV-GAN)
    G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image SegmentationarXiv:2310.16175
  • Retinal Vessel SegmentationonCHASE_DB1
    Sensitivity· 2023-10-24
    0.8584
    best: 0.8798 (FR-UNet)
    G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image SegmentationarXiv:2310.16175
  • Retinal Vessel SegmentationonCHASE_DB1
    mIOU· 2023-10-24
    0.7024
    best: 0.9705 (RV-GAN)
    G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image SegmentationarXiv:2310.16175
  • Retinal Vessel SegmentationonDRIVE
    Accuracy· 2023-10-24
    0.9689
    best: 0.9712 (U-Net)
    G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image SegmentationarXiv:2310.16175
  • Retinal Vessel SegmentationonDRIVE
    F1 score· 2023-10-24
    0.821
    best: 0.8322 (FSG-Net)
    G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image SegmentationarXiv:2310.16175
  • Retinal Vessel SegmentationonDRIVE
    Specificity· 2023-10-24
    0.9822
    best: 0.9844 (MERIT-GCASCADE)
    G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image SegmentationarXiv:2310.16175
  • Retinal Vessel SegmentationonDRIVE
    mIoU· 2023-10-24
    0.697
    best: 0.8406 (FSG-Net)
    G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image SegmentationarXiv:2310.16175
  • Retinal Vessel SegmentationonDRIVE
    sensitivity· 2023-10-24
    0.83
    best: 0.842 (FSG-Net)
    G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image SegmentationarXiv:2310.16175