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Models/MERIT-GCASCADE

MERIT-GCASCADE

Reported on 21 benchmarks across 2 tasks · 1 paper · 3 SOTA

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

Medical24 results

Medical Image SegmentationonCHASE_DB1
DSC· 2023-10-24
0.8267
SOTA
G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation arXiv:2310.16175
Medical Image SegmentationonDRIVE
Specificity· 2023-10-24
0.9844
SOTA
G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation arXiv:2310.16175
Retinal Vessel SegmentationonDRIVE
Specificity· 2023-10-24
0.9844
SOTA
G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation arXiv:2310.16175
Medical Image SegmentationonMICCAI 2015 Multi-Atlas Abdomen Labeling Challenge
Avg DSC· 2023-10-24
84.54
best: 84.9 (MERIT)
G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation arXiv:2310.16175
Medical Image SegmentationonMICCAI 2015 Multi-Atlas Abdomen Labeling Challenge
Avg HD· 2023-10-24
10.38
best: 20.23 (PVT-CASCADE)
G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation arXiv:2310.16175
Medical Image SegmentationonAutomatic Cardiac Diagnosis Challenge (ACDC)
Avg DSC· 2023-10-24
92.23
best: 94.26 (FCT)
G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation arXiv:2310.16175
Medical Image SegmentationonDRIVE
F1 score· 2023-10-24
0.829
best: 0.8322 (FSG-Net)
G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation arXiv:2310.16175
Medical Image SegmentationonDRIVE
Recall· 2023-10-24
0.8281
best: 0.83 (PVT-GCASCADE)
G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation arXiv:2310.16175
Medical Image SegmentationonDRIVE
mIoU· 2023-10-24
0.7081
best: 0.8406 (FSG-Net)
G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation arXiv:2310.16175
Medical Image SegmentationonCHASE_DB1
F1 score· 2023-10-24
0.8267
best: 0.8957 (RV-GAN)
G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation arXiv:2310.16175
Medical Image SegmentationonCHASE_DB1
Sensitivity· 2023-10-24
0.8493
best: 0.8798 (FR-UNet)
G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation arXiv:2310.16175
Medical Image SegmentationonCHASE_DB1
mIOU· 2023-10-24
0.705
best: 0.9705 (RV-GAN)
G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation arXiv:2310.16175
Medical Image SegmentationonDRIVE
Accuracy· 2023-10-24
0.9707
best: 0.9712 (U-Net)
G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation arXiv:2310.16175
Medical Image SegmentationonDRIVE
F1 score· 2023-10-24
0.829
best: 0.8322 (FSG-Net)
G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation arXiv:2310.16175
Medical Image SegmentationonDRIVE
Specificity· 2023-10-24
0.9844
G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation arXiv:2310.16175
Medical Image SegmentationonDRIVE
mIoU· 2023-10-24
0.7081
best: 0.8406 (FSG-Net)
G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation arXiv:2310.16175
Medical Image SegmentationonDRIVE
sensitivity· 2023-10-24
0.8281
best: 0.842 (FSG-Net)
G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation arXiv:2310.16175
Retinal Vessel SegmentationonCHASE_DB1
F1 score· 2023-10-24
0.8267
best: 0.8957 (RV-GAN)
G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation arXiv:2310.16175
Retinal Vessel SegmentationonCHASE_DB1
Sensitivity· 2023-10-24
0.8493
best: 0.8798 (FR-UNet)
G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation arXiv:2310.16175
Retinal Vessel SegmentationonCHASE_DB1
mIOU· 2023-10-24
0.705
best: 0.9705 (RV-GAN)
G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation arXiv:2310.16175
Retinal Vessel SegmentationonDRIVE
Accuracy· 2023-10-24
0.9707
best: 0.9712 (U-Net)
G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation arXiv:2310.16175
Retinal Vessel SegmentationonDRIVE
F1 score· 2023-10-24
0.829
best: 0.8322 (FSG-Net)
G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation arXiv:2310.16175
Retinal Vessel SegmentationonDRIVE
mIoU· 2023-10-24
0.7081
best: 0.8406 (FSG-Net)
G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation arXiv:2310.16175
Retinal Vessel SegmentationonDRIVE
sensitivity· 2023-10-24
0.8281
best: 0.842 (FSG-Net)
G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation arXiv:2310.16175