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SOTA
医療画像セグメンテーション
Medical Image Segmentation On Automatic
Medical Image Segmentation On Automatic
評価指標
Avg DSC
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
Avg DSC
Paper Title
Repository
FCT
94.26
Adaptive t-vMF Dice Loss for Multi-class Medical Image Segmentation
Interactive AI-SAM gt box
93.89
AI-SAM: Automatic and Interactive Segment Anything Model
FCT
93.02
The Fully Convolutional Transformer for Medical Image Segmentation
LHU-Net
92.65
LHU-Net: A Light Hybrid U-Net for Cost-Efficient, High-Performance Volumetric Medical Image Segmentation
MIST
92.56
MIST: Medical Image Segmentation Transformer with Convolutional Attention Mixing (CAM) Decoder
MERIT
92.32
Multi-scale Hierarchical Vision Transformer with Cascaded Attention Decoding for Medical Image Segmentation
MERIT-GCASCADE
92.23
G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation
EMCAD
92.12
EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation
nnFormer
92.06
nnFormer: Interleaved Transformer for Volumetric Segmentation
Automatic AI-SAM
92.06
AI-SAM: Automatic and Interactive Segment Anything Model
PVT-GCASCADE
91.95
G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation
TransCASCADE
91.63
Medical Image Segmentation via Cascaded Attention Decoding
-
PVT-CASCADE
91.46
Medical Image Segmentation via Cascaded Attention Decoding
-
SegFormer3D
90.96
SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentation
TransUNet
90.4
S2S2: Semantic Stacking for Robust Semantic Segmentation in Medical Imaging
SwinUnet
90.00
Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation
TransUNet
89.71
TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
MISSFormer
87.9
MISSFormer: An Effective Medical Image Segmentation Transformer
R50-ViT-CUP
87.57
TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
R50-AttnUNet
86.75
TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
0 of 20 row(s) selected.
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