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Models/Steerable G-CNN (C12)

Steerable G-CNN (C12)

Reported on 6 benchmarks across 3 tasks · 1 paper · 2 SOTA

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

Medical11 results

Multi-tissue Nucleus SegmentationonKumar
Dice· 2017-11-20
0.82
best: 0.843 (GC-MHVN)
SOTA
Learning Steerable Filters for Rotation Equivariant CNNs arXiv:1711.07289
Multi-tissue Nucleus SegmentationonKumar
Hausdorff Distance (mm)· 2017-11-20
55.8
best: 60 (DSF-CNN (C8))
SOTA
Learning Steerable Filters for Rotation Equivariant CNNs arXiv:1711.07289
Breast Tumour ClassificationonPCam
AUC· 2017-11-20
0.969
best: 0.975 (DSF-CNN (C8))
Learning Steerable Filters for Rotation Equivariant CNNs arXiv:1711.07289
Colorectal Gland Segmentation:onCRAG
Dice· 2017-11-20
0.87
best: 0.892 (PatchCL)
Learning Steerable Filters for Rotation Equivariant CNNs arXiv:1711.07289
Colorectal Gland Segmentation:onCRAG
F1-score· 2017-11-20
0.855
best: 0.881 (PatchCL)
Learning Steerable Filters for Rotation Equivariant CNNs arXiv:1711.07289
Colorectal Gland Segmentation:onCRAG
Hausdorff Distance (mm)· 2017-11-20
156.2
best: 318.9 (VF-CNN (C4))
Learning Steerable Filters for Rotation Equivariant CNNs arXiv:1711.07289
Colorectal Gland Segmentation:onCRAG
Dice· 2017-11-20
0.869
best: 0.892 (PatchCL)
Learning Steerable Filters for Rotation Equivariant CNNs arXiv:1711.07289
Colorectal Gland Segmentation:onCRAG
F1-score· 2017-11-20
0.837
best: 0.881 (PatchCL)
Learning Steerable Filters for Rotation Equivariant CNNs arXiv:1711.07289
Colorectal Gland Segmentation:onCRAG
Hausdorff Distance (mm)· 2017-11-20
164.8
best: 318.9 (VF-CNN (C4))
Learning Steerable Filters for Rotation Equivariant CNNs arXiv:1711.07289
Multi-tissue Nucleus SegmentationonKumar
Dice· 2017-11-20
0.818
best: 0.843 (GC-MHVN)
Learning Steerable Filters for Rotation Equivariant CNNs arXiv:1711.07289
Multi-tissue Nucleus SegmentationonKumar
Hausdorff Distance (mm)· 2017-11-20
54.3
best: 60 (DSF-CNN (C8))
Learning Steerable Filters for Rotation Equivariant CNNs arXiv:1711.07289