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Medical Image Segmentation
Medical Image Segmentation On Etis
Medical Image Segmentation On Etis
Metrics
mIoU
mean Dice
Results
Performance results of various models on this benchmark
Columns
Model Name
mIoU
mean Dice
Paper Title
Repository
MEGANet(ResNet-34)
0.709
0.789
MEGANet: Multi-Scale Edge-Guided Attention Network for Weak Boundary Polyp Segmentation
DUCK-Net
0.8788
0.9354
Using DUCK-Net for Polyp Image Segmentation
RSAFormer
-
0.835
RSAFormer: A method of polyp segmentation with region self-attention transformer
DuAT
0.746
0.822
DuAT: Dual-Aggregation Transformer Network for Medical Image Segmentation
SSFormer-L
0.720
0.796
Stepwise Feature Fusion: Local Guides Global
ESFPNet-L
0.748
0.823
ESFPNet: efficient deep learning architecture for real-time lesion segmentation in autofluorescence bronchoscopic video
EMCAD
-
0.9229
EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation
Meta-Polyp
0.704
0.78
Meta-Polyp: a baseline for efficient Polyp segmentation
ProMISe
0.750
0.840
ProMISe: Promptable Medical Image Segmentation using SAM
UACANet-L
0.689
0.766
UACANet: Uncertainty Augmented Context Attention for Polyp Segmentation
PVT-CASCADE
0.7258
0.8007
Medical Image Segmentation via Cascaded Attention Decoding
ResUNet++
0.7534
0.6364
ResUNet++: An Advanced Architecture for Medical Image Segmentation
-
PraNet
0.5670
0.6280
PraNet: Parallel Reverse Attention Network for Polyp Segmentation
MEGANet(Res2Net-50)
0.665
0.739
MEGANet: Multi-Scale Edge-Guided Attention Network for Weak Boundary Polyp Segmentation
HarDNet-MSEG
0.613
0.677
HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPS
COMMA (Res2Net-50)
0.648
0.711
COMMA: Propagating Complementary Multi-Level Aggregation Network for Polyp Segmentation
-
RAPUNet
0.9179
0.9572
MetaFormer and CNN Hybrid Model for Polyp Image Segmentation
TransFuse-L
0.661
0.737
TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation
TransFuse-S
0.659
0.733
TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation
HarDNet-DFUS
-
0.730
HarDNet-DFUS: An Enhanced Harmonically-Connected Network for Diabetic Foot Ulcer Image Segmentation and Colonoscopy Polyp Segmentation
0 of 24 row(s) selected.
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