Medical Image Classification on RSNA (AUC)
94.5AUCMed-UniC (ViT-L/32)
Evaluation Results
| Method | Links | |
|---|---|---|
| Med-UniC (ViT-L/32)Backbone=ViT-L/32, Training Data=100%, Protocol=Linear classification2023.05 | 94.5 | |
| Med-UniC (ViT-L/32)Backbone=ViT-L/32, Training Data=10%, Protocol=Linear classification2023.05 | 93.8 | |
| Med-UniC (ViT-B/16)Backbone=ViT-B/16, Training Data=100%, Protocol=Linear classification2023.05 | 93.7 | |
| MRMBackbone=ViT, Training Data=100%, Protocol=Linear classification2023.05 | 93.3 | |
| Med-UniC (ViT-B/16)Backbone=ViT-B/16, Training Data=10%, Protocol=Linear classification2023.05 | 93.1 | |
| X-WINPretrain. data=372K CXRs + 32K CT volumes, Backbone=ViT-Large, Evaluation protocol=linear probing2025.11 | 92.9 | |
| MRMBackbone=ViT, Training Data=10%, Protocol=Linear classification2023.05 | 92.7 | |
| Med-UniC (ViT-L/32)Backbone=ViT-L/32, Training Data=1%, Protocol=Linear classification2023.05 | 92.2 | |
| Med-UniC (ViT-B/16)Backbone=ViT-B/16, Training Data=1%, Protocol=Linear classification2023.05 | 91.9 | |
| X-WINPretrain. data=372K CXRs + 32K CT volumes, Backbone=ViT-Base, Evaluation protocol=linear probing2025.11 | 91.7 | |
| MRMBackbone=ViT, Training Data=1%, Protocol=Linear classification2023.05 | 91.3 | |
| Med-UniC (ResNet-50)Backbone=ResNet-50, Training Data=100%, Protocol=Linear classification2023.05 | 90.8 | |
| MGCA*Backbone=ViT-B/16, Training Data=100%, Protocol=Linear classification, disease-level annotations=true2023.05 | 90.8 | |
| Med-UniC (ResNet-50)Backbone=ResNet-50, Training Data=10%, Protocol=Linear classification2023.05 | 90.4 | |
| GLoRIA-MIMICBackbone=ResNet-50, Training Data=100%, Protocol=Linear classification2023.05 | 90.2 | |
| MGCA*Backbone=ResNet-50, Training Data=100%, Protocol=Linear classification, disease-level annotations=true2023.05 | 89.9 | |
| MGCA*Backbone=ViT-B/16, Training Data=10%, Protocol=Linear classification, disease-level annotations=true2023.05 | 89.9 | |
| GLoRIA-MIMICBackbone=ResNet-50, Training Data=10%, Protocol=Linear classification2023.05 | 89.4 | |
| MedKLIP*Backbone=ResNet-50, Training Data=100%, Protocol=Linear classification, disease-level annotations=true2023.05 | 89.3 | |
| Ark+Pretrain. data=704K CXRs, Backbone=Swin-Base, Evaluation protocol=linear probing2025.11 | 89.3 | |
| MGCA*Backbone=ResNet-50, Training Data=10%, Protocol=Linear classification, disease-level annotations=true2023.05 | 89.1 | |
| Med-UniC (ResNet-50)Backbone=ResNet-50, Training Data=1%, Protocol=Linear classification2023.05 | 89.1 | |
| MGCA*Backbone=ViT-B/16, Training Data=1%, Protocol=Linear classification, disease-level annotations=true2023.05 | 89.1 | |
| GLoRIABackbone=ResNet-50, Training Data=100%, Protocol=Linear classification2023.05 | 88.6 | |
| MGCA*Backbone=ResNet-50, Training Data=1%, Protocol=Linear classification, disease-level annotations=true2023.05 | 88.6 | |
| GLoRIABackbone=ResNet-50, Training Data=10%, Protocol=Linear classification2023.05 | 88 | |
| MedKLIP*Backbone=ResNet-50, Training Data=10%, Protocol=Linear classification, disease-level annotations=true2023.05 | 88 | |
| MedKLIP*Backbone=ResNet-50, Training Data=1%, Protocol=Linear classification, disease-level annotations=true2023.05 | 87.3 | |
| CheXFoundPretrain. data=987K CXRs, Backbone=ViT-Large, Evaluation protocol=linear probing2025.11 | 87.2 | |
| GLoRIA-MIMICBackbone=ResNet-50, Training Data=1%, Protocol=Linear classification2023.05 | 87 | |
| RAD-DINOPretrain. data=LVD-142M + 838K CXRs, Backbone=ViT-Base, Evaluation protocol=linear probing2025.11 | 86.9 | |
| GLoRIABackbone=ResNet-50, Training Data=1%, Protocol=Linear classification2023.05 | 86.1 | |
| CXR-AlignPretrain. data=325K CXRs w/ reports, Backbone=ViT-Base, Evaluation protocol=linear probing2025.11 | 85.4 | |
| CARZeroPretrain. data=377K CXRs w/ reports, Backbone=ViT-Base, Evaluation protocol=linear probing2025.11 | 83.3 | |
| CheXWorldPretrain. data=448K CXRs, Backbone=ViT-Base, Evaluation protocol=linear probing2025.11 | 82.4 | |
| MaCoPretrain. data=377K CXRs w/ reports, Backbone=ViT-Base, Evaluation protocol=linear probing2025.11 | 82.2 | |
| ConVIRTBackbone=ResNet-50, Training Data=100%, Protocol=Linear classification2023.05 | 81.3 | |
| ConVIRTBackbone=ResNet-50, Training Data=10%, Protocol=Linear classification2023.05 | 80.1 | |
| DINOv2Pretrain. data=LVD-142M, Backbone=ViT-Base, Evaluation protocol=linear probing2025.11 | 79.8 | |
| CheXAgentPretrain. data=1.07M CXRs w/ text, Backbone=ViT-Large, Evaluation protocol=linear probing2025.11 | 78.9 | |
| ConVIRTBackbone=ResNet-50, Training Data=1%, Protocol=Linear classification2023.05 | 77.4 | |
| ImageNet InitTraining Data=100%, Protocol=Linear classification2023.05 | 76.3 | |
| I-JEPAPretrain. data=ImageNet-1K, Backbone=ViT-Large, Evaluation protocol=linear probing2025.11 | 75.4 | |
| ImageNet InitTraining Data=1%, Protocol=Linear classification2023.05 | 74.9 | |
| ImageNet InitTraining Data=10%, Protocol=Linear classification2023.05 | 74.5 | |
| Random InitTraining Data=100%, Protocol=Linear classification2023.05 | 74.1 | |
| Random InitTraining Data=10%, Protocol=Linear classification2023.05 | 69.4 | |
| Random InitTraining Data=1%, Protocol=Linear classification2023.05 | 58.9 |