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SOTA
Classification des maladies de la rétine par OCT
Retinal Oct Disease Classification On Oct2017
Retinal Oct Disease Classification On Oct2017
Métriques
Acc
Sensitivity
Résultats
Résultats de performance de divers modèles sur ce benchmark
Columns
Nom du modèle
Acc
Sensitivity
Paper Title
Repository
InceptionV3 (limited)
93.4
96.6
Rethinking the Inception Architecture for Computer Vision
-
InceptionV3
96.6
97.8
Rethinking the Inception Architecture for Computer Vision
-
MobileNet-v2
99.4
99.4
Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images
-
Xception
99.7
99.7
Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images
-
WideResNet-50-2 (EMA-decay=0.999)
99.69
-
Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few Labels
-
MobileNet-v2
98.5
99.4
MobileNetV2: Inverted Residuals and Linear Bottlenecks
-
Joint-Attention-Network OpticNet-71
77.4
-
Improving Robustness using Joint Attention Network For Detecting Retinal Degeneration From Optical Coherence Tomography Images
-
ResNet50-v1
99.3
99.3
Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images
-
Joint-Attention-Network MobileNet-v2
95.6
-
Improving Robustness using Joint Attention Network For Detecting Retinal Degeneration From Optical Coherence Tomography Images
-
OpticNet-71
99.8
99.8
Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images
-
ResNet50-v1
99.3
99.3
Deep Residual Learning for Image Recognition
-
InceptionV3 (limited)
93.4
96.6
Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images
-
Xception
-
99.7
Xception: Deep Learning With Depthwise Separable Convolutions
InceptionV3
96.6
97.8
Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images
-
Joint-Attention-Network ResNet50-v1
92.4
-
Improving Robustness using Joint Attention Network For Detecting Retinal Degeneration From Optical Coherence Tomography Images
-
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