Retinal Oct Disease Classification On Oct2017

Metrics

Acc

Sensitivity

Results

Performance results of various models on this benchmark

			Paper Title
OpticNet-71	99.8	99.8	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	99.4	99.4	Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images
ResNet50-v1	99.3	99.3	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
MobileNet-v2	98.5	99.4	MobileNetV2: Inverted Residuals and Linear Bottlenecks
InceptionV3	96.6	97.8	Rethinking the Inception Architecture for Computer Vision
InceptionV3	96.6	97.8	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
InceptionV3 (limited)	93.4	96.6	Rethinking the Inception Architecture for Computer Vision
InceptionV3 (limited)	93.4	96.6	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
Joint-Attention-Network OpticNet-71	77.4	-	Improving Robustness using Joint Attention Network For Detecting Retinal Degeneration From Optical Coherence Tomography Images
Xception	-	99.7	Xception: Deep Learning With Depthwise Separable Convolutions

0 of 15 row(s) selected.

Retinal Oct Disease Classification On Oct2017

Metrics

Acc

Sensitivity

Results

Performance results of various models on this benchmark

			Paper Title
OpticNet-71	99.8	99.8	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	99.4	99.4	Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images
ResNet50-v1	99.3	99.3	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
MobileNet-v2	98.5	99.4	MobileNetV2: Inverted Residuals and Linear Bottlenecks
InceptionV3	96.6	97.8	Rethinking the Inception Architecture for Computer Vision
InceptionV3	96.6	97.8	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
InceptionV3 (limited)	93.4	96.6	Rethinking the Inception Architecture for Computer Vision
InceptionV3 (limited)	93.4	96.6	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
Joint-Attention-Network OpticNet-71	77.4	-	Improving Robustness using Joint Attention Network For Detecting Retinal Degeneration From Optical Coherence Tomography Images
Xception	-	99.7	Xception: Deep Learning With Depthwise Separable Convolutions

0 of 15 row(s) selected.