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Models/BCDU-Net (d=3)

BCDU-Net (d=3)

Reported on 7 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.

Medical7 results

Medical Image SegmentationonISIC 2018
F1-Score· 2019-08-31
0.851
SOTA
Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions arXiv:1909.00166
Medical Image SegmentationonLUNA
AUC· 2019-08-31
0.9946
SOTA
Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions arXiv:1909.00166
Medical Image SegmentationonLUNA
F1 score· 2019-08-31
0.9904
SOTA
Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions arXiv:1909.00166
Medical Image SegmentationonDRIVE
AUC· 2019-08-31
0.9789
best: 0.9931 (Swin-Res-Net)
Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions arXiv:1909.00166
Medical Image SegmentationonDRIVE
F1 score· 2019-08-31
0.8224
best: 0.8322 (FSG-Net)
Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions arXiv:1909.00166
Retinal Vessel SegmentationonDRIVE
AUC· 2019-08-31
0.9789
best: 0.9931 (Swin-Res-Net)
Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions arXiv:1909.00166
Retinal Vessel SegmentationonDRIVE
F1 score· 2019-08-31
0.8224
best: 0.8322 (FSG-Net)
Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions arXiv:1909.00166