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プラットフォーム
ホーム
SOTA
網膜血管セグメンテーション
Retinal Vessel Segmentation On Drive
Retinal Vessel Segmentation On Drive
評価指標
AUC
F1 score
評価結果
このベンチマークにおける各モデルのパフォーマンス結果
Columns
モデル名
AUC
F1 score
Paper Title
FSG-Net
0.9823
0.8322
Full-scale Representation Guided Network for Retinal Vessel Segmentation
Study Group Learning
0.9886
0.8316
Study Group Learning: Improving Retinal Vessel Segmentation Trained with Noisy Labels
FR-UNet
0.9889
0.8316
Full-Resolution Network and Dual-Threshold Iteration for Retinal Vessel and Coronary Angiograph Segmentation
MERIT-GCASCADE
-
0.8290
G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation
SA-UNet
0.9864
0.8263
SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation
VGN
0.9802
0.8263
Deep Vessel Segmentation By Learning Graphical Connectivity
ConvMixer
-
0.8245
Deep Learning Architectures for Diagnosis of Diabetic Retinopathy
DUNet
0.9802
0.8237
DUNet: A deformable network for retinal vessel segmentation
BCDU-Net (d=3)
0.9789
0.8224
Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions
ConvMixer-Light
-
0.8215
Deep Learning Architectures for Diagnosis of Diabetic Retinopathy
PVT-GCASCADE
-
0.8210
G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation
IterNet
0.9816
0.8205
IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks
LadderNet
0.9793
0.8202
LadderNet: Multi-path networks based on U-Net for medical image segmentation
Residual U-Net
0.9779
0.8149
Road Extraction by Deep Residual U-Net
U-Net
0.9755
0.8142
U-Net: Convolutional Networks for Biomedical Image Segmentation
DR_2021
-
0.75
Segmentation of Blood Vessels, Optic Disc Localization, Detection of Exudates and Diabetic Retinopathy Diagnosis from Digital Fundus Images
ET-Net
-
-
ET-Net: A Generic Edge-aTtention Guidance Network for Medical Image Segmentation
RV-GAN
-
-
RV-GAN: Segmenting Retinal Vascular Structure in Fundus Photographs using a Novel Multi-scale Generative Adversarial Network
U-Net
0.9855
-
Exploring The Limits Of Data Augmentation For Retinal Vessel Segmentation
CE-Net
0.9779
-
CE-Net: Context Encoder Network for 2D Medical Image Segmentation
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