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Multi Tissue Nucleus Segmentation On Kumar

Métriques

Dice

Hausdorff Distance (mm)

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	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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