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Medical Image Segmentation On 2018 Data

Métriques

Dice

Precision

Recall

mIoU

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Dice	Precision	Recall	mIoU	Paper Title	Repository
MSRF-Net	0.9224	0.9022	0.9402	0.8534	MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation
EMCAD	0.9274	-	-	-	EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation
Trans2Unet	0.9225	-	-	0.8614	Trans2Unet: Neural fusion for Nuclei Semantic Segmentation	-
Unet++	0.8974	-	-	0.9255	UNet++: A Nested U-Net Architecture for Medical Image Segmentation
FANet	0.9176	0.9194	0.9222	0.8569	FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation
DoubleUNet	0.9133	0.9596	0.6407	0.8407	DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation
DCSAU-Net	-	-	0.9240	0.8501	DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentation	-
SSFormer-L	0.9230	-	-	0.8614	Stepwise Feature Fusion: Local Guides Global
DuAT	0.926	-	-	0.870	DuAT: Dual-Aggregation Transformer Network for Medical Image Segmentation

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