Retinal Oct Disease Classification On Oct2017

评估指标

Acc
Sensitivity

评测结果

各个模型在此基准测试上的表现结果

模型名称
Acc
Sensitivity
Paper TitleRepository
InceptionV3 (limited)93.496.6Rethinking the Inception Architecture for Computer Vision-
InceptionV396.697.8Rethinking the Inception Architecture for Computer Vision-
MobileNet-v299.499.4Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images-
Xception99.799.7Optic-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-v298.599.4MobileNetV2: Inverted Residuals and Linear Bottlenecks-
Joint-Attention-Network OpticNet-7177.4-Improving Robustness using Joint Attention Network For Detecting Retinal Degeneration From Optical Coherence Tomography Images-
ResNet50-v199.399.3Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images-
Joint-Attention-Network MobileNet-v295.6-Improving Robustness using Joint Attention Network For Detecting Retinal Degeneration From Optical Coherence Tomography Images-
OpticNet-7199.899.8Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images-
ResNet50-v199.399.3Deep Residual Learning for Image Recognition-
InceptionV3 (limited)93.496.6Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images-
Xception-99.7Xception: Deep Learning With Depthwise Separable Convolutions
InceptionV396.697.8Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images-
Joint-Attention-Network ResNet50-v192.4-Improving Robustness using Joint Attention Network For Detecting Retinal Degeneration From Optical Coherence Tomography Images-
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