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Retinal Vessel Segmentation On Chase_Db1

Metrics

AUC

Acc

F1 score

MCC

Sensitivity

mIOU

Results

Performance results of various models on this benchmark

Model Name	AUC	Acc	F1 score	MCC	Sensitivity	mIOU	Paper Title	Repository
FSG-Net	0.9937	0.9751	0.8101	0.7989	0.8599	0.8268	Full-scale Representation Guided Network for Retinal Vessel Segmentation	-
IterNet	0.9851	-	0.8073	-	-	-	IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks	-
MERIT-GCASCADE	-	-	0.8267	-	0.8493	0.7050	G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation	-
LadderNet	0.9839	-	0.8031	-	-	-	LadderNet: Multi-path networks based on U-Net for medical image segmentation	-
U-Net ASPP	-	-	-	-	-	0.8959	Resolution-Aware Design of Atrous Rates for Semantic Segmentation Networks	-
SA-UNet	0.9905	-	0.8153	-	-	-	SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation	-
Residual U-Net	0.9779	-	0.7800	-	-	-	Road Extraction by Deep Residual U-Net	-
VGN	0.9830	-	0.8034	-	-	-	Deep Vessel Segmentation By Learning Graphical Connectivity	-
DUNet	0.9804	-	0.7883	-	-	-	DUNet: A deformable network for retinal vessel segmentation	-
R2U-Net	0.9815	-	0.7928	-	-	-	Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation	-
RV-GAN	0.9914	-	0.8957	-	0.8199	0.9705	RV-GAN: Segmenting Retinal Vascular Structure in Fundus Photographs using a Novel Multi-scale Generative Adversarial Network	-
Study Group Learning	0.9920	-	0.8271	-	0.8690	-	Study Group Learning: Improving Retinal Vessel Segmentation Trained with Noisy Labels	-
U-Net	0.9772	-	-	-	-	-	U-Net: Convolutional Networks for Biomedical Image Segmentation	-
PVT-GCASCADE	-	-	0.8251	-	0.8584	0.7024	G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation	-
FR-UNet	0.9913	-	0.8151	-	0.8798	-	Full-Resolution Network and Dual-Threshold Iteration for Retinal Vessel and Coronary Angiograph Segmentation

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Retinal Vessel Segmentation On Chase_Db1 | SOTA | HyperAI