Medical Image Classification on COVIDx
95.2AccuracyMed-UniC (ViT-L/32)
Evaluation Results
| Method | Links | |
|---|---|---|
| Med-UniC (ViT-L/32)Backbone=ViT-L/32, Training Data=100%, Protocol=Linear classification2023.05 | 95.2 | |
| Med-UniC (ViT-B/16)Backbone=ViT-B/16, Training Data=100%, Protocol=Linear classification2023.05 | 94.5 | |
| Med-UniC (ResNet-50)Backbone=ResNet-50, Training Data=100%, Protocol=Linear classification2023.05 | 92.8 | |
| MGCA*Backbone=ViT-B/16, Training Data=100%, Protocol=Linear classification, disease-level annotations=true2023.05 | 92.3 | |
| ConVIRTBackbone=ResNet-50, Training Data=100%, Protocol=Linear classification2023.05 | 92 | |
| Med-UniC (ViT-L/32)Backbone=ViT-L/32, Training Data=10%, Protocol=Linear classification2023.05 | 91.8 | |
| MRMBackbone=ViT, Training Data=100%, Protocol=Linear classification2023.05 | 90.8 | |
| MGCA*Backbone=ResNet-50, Training Data=100%, Protocol=Linear classification, disease-level annotations=true2023.05 | 90.5 | |
| MedKLIP*Backbone=ResNet-50, Training Data=100%, Protocol=Linear classification, disease-level annotations=true2023.05 | 90.3 | |
| Med-UniC (ViT-B/16)Backbone=ViT-B/16, Training Data=10%, Protocol=Linear classification2023.05 | 89.5 | |
| GLoRIABackbone=ResNet-50, Training Data=100%, Protocol=Linear classification2023.05 | 89 | |
| Med-UniC (ResNet-50)Backbone=ResNet-50, Training Data=10%, Protocol=Linear classification2023.05 | 89 | |
| GLoRIA-MIMICBackbone=ResNet-50, Training Data=100%, Protocol=Linear classification2023.05 | 88.8 | |
| ImageNet InitTraining Data=100%, Protocol=Linear classification2023.05 | 86.3 | |
| MedKLIP*Backbone=ResNet-50, Training Data=10%, Protocol=Linear classification, disease-level annotations=true2023.05 | 85.2 | |
| MGCA*Backbone=ViT-B/16, Training Data=10%, Protocol=Linear classification, disease-level annotations=true2023.05 | 84.8 | |
| MGCA*Backbone=ResNet-50, Training Data=10%, Protocol=Linear classification, disease-level annotations=true2023.05 | 83.5 | |
| ConVIRTBackbone=ResNet-50, Training Data=10%, Protocol=Linear classification2023.05 | 82.5 | |
| Med-UniC (ViT-L/32)Backbone=ViT-L/32, Training Data=1%, Protocol=Linear classification2023.05 | 81.5 | |
| GLoRIA-MIMICBackbone=ResNet-50, Training Data=10%, Protocol=Linear classification2023.05 | 80.5 | |
| Med-UniC (ViT-B/16)Backbone=ViT-B/16, Training Data=1%, Protocol=Linear classification2023.05 | 80.3 | |
| MRMBackbone=ViT, Training Data=10%, Protocol=Linear classification2023.05 | 79.3 | |
| ImageNet InitTraining Data=10%, Protocol=Linear classification2023.05 | 78.8 | |
| GLoRIABackbone=ResNet-50, Training Data=10%, Protocol=Linear classification2023.05 | 77.8 | |
| Med-UniC (ResNet-50)Backbone=ResNet-50, Training Data=1%, Protocol=Linear classification2023.05 | 76.5 | |
| MGCA*Backbone=ViT-B/16, Training Data=1%, Protocol=Linear classification, disease-level annotations=true2023.05 | 74.8 | |
| MedKLIP*Backbone=ResNet-50, Training Data=1%, Protocol=Linear classification, disease-level annotations=true2023.05 | 74.5 | |
| ConVIRTBackbone=ResNet-50, Training Data=1%, Protocol=Linear classification2023.05 | 72.5 | |
| MGCA*Backbone=ResNet-50, Training Data=1%, Protocol=Linear classification, disease-level annotations=true2023.05 | 72 | |
| Random InitTraining Data=100%, Protocol=Linear classification2023.05 | 70 | |
| GLoRIABackbone=ResNet-50, Training Data=1%, Protocol=Linear classification2023.05 | 67.3 | |
| MRMBackbone=ViT, Training Data=1%, Protocol=Linear classification2023.05 | 66.9 | |
| GLoRIA-MIMICBackbone=ResNet-50, Training Data=1%, Protocol=Linear classification2023.05 | 66.5 | |
| ImageNet InitTraining Data=1%, Protocol=Linear classification2023.05 | 64.8 | |
| Random InitTraining Data=10%, Protocol=Linear classification2023.05 | 60.3 | |
| Random InitTraining Data=1%, Protocol=Linear classification2023.05 | 50.5 |