Retinal Vessel Segmentation On Chase_Db1

평가 지표

AUC
Acc
F1 score
MCC
Sensitivity
mIOU

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름
AUC
Acc
F1 score
MCC
Sensitivity
mIOU
Paper TitleRepository
FSG-Net0.99370.97510.81010.79890.85990.8268Full-scale Representation Guided Network for Retinal Vessel Segmentation-
IterNet0.9851-0.8073---IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks-
MERIT-GCASCADE--0.8267-0.84930.7050G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation-
LadderNet0.9839-0.8031---LadderNet: Multi-path networks based on U-Net for medical image segmentation-
U-Net ASPP-----0.8959Resolution-Aware Design of Atrous Rates for Semantic Segmentation Networks-
SA-UNet0.9905-0.8153---SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation-
Residual U-Net0.9779-0.7800---Road Extraction by Deep Residual U-Net-
VGN0.9830-0.8034---Deep Vessel Segmentation By Learning Graphical Connectivity-
DUNet0.9804-0.7883---DUNet: A deformable network for retinal vessel segmentation-
R2U-Net0.9815-0.7928---Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation-
RV-GAN0.9914-0.8957-0.81990.9705RV-GAN: Segmenting Retinal Vascular Structure in Fundus Photographs using a Novel Multi-scale Generative Adversarial Network-
Study Group Learning0.9920-0.8271-0.8690-Study Group Learning: Improving Retinal Vessel Segmentation Trained with Noisy Labels-
U-Net0.9772-----U-Net: Convolutional Networks for Biomedical Image Segmentation-
PVT-GCASCADE--0.8251-0.85840.7024G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation-
FR-UNet0.9913-0.8151-0.8798-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 | HyperAI초신경