Medical Image Segmentation on CHASE_DB1

Metric: Sensitivity (higher is better)

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#	Model↕	Sensitivity▼	Extra Data	Paper	Date↕	Code
1	FR-UNet	0.8798	No	-	-	Code
2	Study Group Learning	0.869	No	Study Group Learning: Improving Retinal Vessel S...	2021-03-05	Code
3	FSG-Net	0.8599	No	Full-scale Representation Guided Network for Ret...	2025-01-31	Code
4	PVT-GCASCADE	0.8584	No	G-CASCADE: Efficient Cascaded Graph Convolutiona...	2023-10-24	Code
5	MERIT-GCASCADE	0.8493	No	G-CASCADE: Efficient Cascaded Graph Convolutiona...	2023-10-24	Code
6	RV-GAN	0.8199	No	RV-GAN: Segmenting Retinal Vascular Structure in...	2021-01-03	Code

#1FR-UNet
0.8798
Sensitivity
No paperCode
#2Study Group LearningSOTA
0.869
Sensitivity· 2021-03-05
Study Group Learning: Improving Retinal Vessel Segmentation Trained with Noisy Labels Code
#3FSG-Net
0.8599
Sensitivity· 2025-01-31
Full-scale Representation Guided Network for Retinal Vessel Segmentation Code
#4PVT-GCASCADE
0.8584
Sensitivity· 2023-10-24
G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation Code
#5MERIT-GCASCADE
0.8493
Sensitivity· 2023-10-24
G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation Code
#6RV-GANSOTA
0.8199
Sensitivity· 2021-01-03
RV-GAN: Segmenting Retinal Vascular Structure in Fundus Photographs using a Novel Multi-scale Generative Adversarial Network Code