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SOTA
Multi Tissue Nucleus Segmentation
Multi Tissue Nucleus Segmentation On Kumar
Multi Tissue Nucleus Segmentation On Kumar
評価指標
Dice
Hausdorff Distance (mm)
評価結果
このベンチマークにおける各モデルのパフォーマンス結果
Columns
モデル名
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
-
0 of 18 row(s) selected.
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