Rotation equivariant vector field networks

ICCV 2017 Diego MarcosMichele VolpiNikos KomodakisDevis Tuia

In many computer vision tasks, we expect a particular behavior of the output with respect to rotations of the input image. If this relationship is explicitly encoded, instead of treated as any other variation, the complexity of the problem is decreased, leading to a reduction in the size of the required model... (read more)

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TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Colorectal Gland Segmentation: CRAG VF-CNN (C8) F1-score 0.745 # 12
Dice 0.758 # 13
Hausdorff Distance (mm) 287.5 # 2
Colorectal Gland Segmentation: CRAG VF-CNN (C4) F1-score 0.711 # 13
Dice 0.721 # 14
Hausdorff Distance (mm) 318.9 # 1

Results from Other Papers


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK SOURCE PAPER COMPARE
Colorectal Gland Segmentation: CRAG VF-CNN (C12) F1-score 0.776 # 11
Dice 0.782 # 12
Hausdorff Distance (mm) 251.9 # 3
Multi-tissue Nucleus Segmentation Kumar VF-CNN (C12) Dice 0.808 # 8
Hausdorff Distance (mm) 50.7 # 12
Dice 0.813 # 5
Hausdorff Distance (mm) 51.4 # 9
Multi-tissue Nucleus Segmentation Kumar VF-CNN (C4) Dice 0.800 # 9
Hausdorff Distance (mm) 49.9 # 13
Breast Tumour Classification PCam VF-CNN (C4) AUC 0.871 # 13
Breast Tumour Classification PCam VF-CNN (C12) AUC 0.898 # 11
Breast Tumour Classification PCam VF-CNN (C8) AUC 0.881 # 12

Methods used in the Paper