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Multi Tissue Nucleus Segmentation On Kumar

Metrics

Dice

Hausdorff Distance (mm)

Results

Performance results of various models on this benchmark

Model Name	Dice	Hausdorff Distance (mm)	Paper Title	Repository
DSF-CNN (C8)	0.826	60	Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images
G-CNN (C12)	0.814	53.4	Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysis
Steerable G-CNN (e)	0.791	51.0	Learning Steerable Filters for Rotation Equivariant CNNs	-
CIA-Net (e)	0.818	57.7	CIA-Net: Robust Nuclei Instance Segmentation with Contour-aware Information Aggregation	-
Mask R-CNN (e)	0.760	50.9	Mask R-CNN
HoVer-Net (e)	0.826	59.7	HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images
Steerable G-CNN (C12)	0.818	54.3	Learning Steerable Filters for Rotation Equivariant CNNs	-
G-CNN (C12)	0.811	51.9	Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysis
U-Net (e)	0.758	47.8	U-Net: Convolutional Networks for Biomedical Image Segmentation
VF-CNN (C4)	0.800	49.9	Rotation equivariant vector field networks
Micro-Net (e)	0.797	51.9	Micro-Net: A unified model for segmentation of various objects in microscopy images	-
Steerable G-CNN (C4)	0.809	54.2	Learning Steerable Filters for Rotation Equivariant CNNs	-
VF-CNN (C12)	0.808	50.7	Rotation equivariant vector field networks
VF-CNN (C12)	0.813	51.4	Rotation equivariant vector field networks
G-CNN (C4)	0.793	49.0	Group Equivariant Convolutional Networks
GC-MHVN	0.843	-	MRL: Learning to Mix with Attention and Convolutions	-
FCN8 (e)	0.797	31.2	Fully Convolutional Networks for Semantic Segmentation
Steerable G-CNN (C12)	0.820	55.8	Learning Steerable Filters for Rotation Equivariant CNNs	-

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