Medical Image Classification on PathMNIST
99.38AccuracyFull dataset
Evaluation Results
| Method | Links | |||
|---|---|---|---|---|
| Full datasetSelection ratio (β)=100%2026.03 | 99.38 | — | — | |
| DUCSSelection ratio (β)=30%2026.03 | 98.46 | — | — | |
| MedVIT-L2026.05 | 98.4 | 98.4 | — | |
| DUCSSelection ratio (β)=20%2026.03 | 96.7 | — | — | |
| MO-MAE2026.05 | 95.3 | 99.7 | — | |
| MedVIT-S2026.05 | 94.2 | 99.3 | — | |
| MedVIT-T2026.05 | 93.8 | 99.4 | — | |
| DUCSSelection ratio (β)=10%2026.03 | 93.49 | — | — | |
| BYOLBackbone=ResNet-50, Initialization=Random, Training Strategy=Self-supervised2026.04 | 93.36 | — | — | |
| SimCLRBackbone=ResNet-50, Initialization=Random, Training Strategy=Self-supervised2026.04 | 92.92 | — | — | |
| NNCLRBackbone=ResNet-50, Initialization=Random, Training Strategy=Self-supervised2026.04 | 92.72 | — | — | |
| MoCoV3Backbone=ResNet-50, Initialization=Random, Training Strategy=Self-supervised2026.04 | 92.5 | — | — | |
| Barlow TwinsBackbone=ResNet-50, Initialization=Random, Training Strategy=Self-supervised2026.04 | 92.43 | — | — | |
| VICRegBackbone=ResNet-50, Initialization=Random, Training Strategy=Self-supervised2026.04 | 92.32 | — | — | |
| DINOBackbone=ResNet-50, Initialization=Random, Training Strategy=Self-supervised2026.04 | 92.03 | — | — | |
| ReSSLBackbone=ResNet-50, Initialization=Random, Training Strategy=Self-supervised2026.04 | 91.99 | — | — | |
| ResNet-50Resolution=282026.02 | 91.1 | 99 | — | |
| EVASelection ratio (β)=30%2026.03 | 91.1 | — | — | |
| ResNet-18Resolution=2242026.02 | 90.9 | 98.9 | — | |
| ResNet-182026.05 | 90.9 | 98.9 | — | |
| ResNet-18Resolution=282026.02 | 90.7 | 98.3 | — | |
| TDDSSelection ratio (β)=30%2026.03 | 90.6 | — | — | |
| TDDSSelection ratio (β)=20%2026.03 | 90.19 | — | — | |
| DUCSSelection ratio (β)=5%2026.03 | 90.02 | — | — | |
| ResNet-50Resolution=2242026.02 | 89.2 | 98.9 | — | |
| ResNet-502026.05 | 89.2 | 98.9 | — | |
| Moderate-DSSelection ratio (β)=30%2026.03 | 88.22 | — | — | |
| EVASelection ratio (β)=20%2026.03 | 87.53 | — | — | |
| Moderate-DSSelection ratio (β)=20%2026.03 | 84.79 | — | — | |
| AutoKeras2026.02 | 83.4 | 95.9 | — | |
| AutoKeras2026.05 | 83.4 | 95.9 | — | |
| DUCSSelection ratio (β)=2%2026.03 | 82.91 | — | — | |
| EVASelection ratio (β)=10%2026.03 | 82.76 | — | — | |
| AMIMV-SSLBackbone=ResNet-50, Initialization=Random, Training Strategy=Self-supervised2026.04 | 82.59 | — | — | |
| TDDSSelection ratio (β)=10%2026.03 | 82.51 | — | — | |
| BioVLMTrain Params=30K2026.04 | 81.56 | — | — | |
| EVASelection ratio (β)=5%2026.03 | 77.9 | — | — | |
| TDDSSelection ratio (β)=5%2026.03 | 77.59 | — | — | |
| CoOpTrain Params=3K2026.04 | 76.93 | — | — | |
| KgCoOpTrain Params=3K2026.04 | 76.61 | — | — | |
| PromptSRCTrain Params=69K2026.04 | 76.42 | — | — | |
| TCPTrain Params=0.5M2026.04 | 76.21 | — | — | |
| CoCoOpTrain Params=44K2026.04 | 75.92 | — | — | |
| MaPLeTrain Params=5.3M2026.04 | 75.5 | — | — | |
| G-LoG bi-filtration (MLP)Sigma (σ)=02026.02 | 75.3 | 95.5 | — | |
| G-LoG bi-filtration (MLP)Sigma (σ)=0.52026.02 | 75.3 | 95.4 | — | |
| EVASelection ratio (β)=2%2026.03 | 75.19 | — | — | |
| Moderate-DSSelection ratio (β)=10%2026.03 | 74.12 | — | — | |
| TDDSSelection ratio (β)=2%2026.03 | 73.4 | — | — | |
| Google AutoML Vision2026.02 | 72.8 | 94.4 | — | |
| Google AutoML2026.05 | 72.8 | 94.4 | — | |
| BiomedCoOpTrain Params=3K2026.04 | 72.59 | — | — | |
| auto-sklearn2026.02 | 71.6 | 93.4 | — | |
| Auto-sklearn2026.05 | 71.6 | 93.4 | — | |
| G-LoG bi-filtration (MLP)Sigma (σ)=12026.02 | 70.8 | 94 | — | |
| G-LoG bi-filtration (MLP)Sigma (σ)=1.52026.02 | 70.2 | 93.9 | — | |
| Topo-Med (MLP)2026.02 | 68.3 | 94.2 | — | |
| Moderate-DSSelection ratio (β)=5%2026.03 | 67.62 | — | — | |
| ProGradTrain Params=3K2026.04 | 60.26 | — | — | |
| Moderate-DSSelection ratio (β)=2%2026.03 | 52.81 | — | — | |
| BioMedCLIPTrain Params=02026.04 | 43.3 | — | — | |
| AMIMV-SSLBackbone=ResNet-50, Initialization=random, Training Protocol=self-supervised2026.04 | — | 97.96 | — | |
| Barlow TwinsBackbone=ResNet-50, Initialization=random, Training Protocol=self-supervised2026.04 | — | 99.38 | — | |
| BYOLBackbone=ResNet-50, Initialization=random, Training Protocol=self-supervised2026.04 | — | 99.44 | — | |
| DINOBackbone=ResNet-50, Initialization=random, Training Protocol=self-supervised2026.04 | — | 99.28 | — | |
| EL2NRatio=2%2026.06 | — | — | 36.8 | |
| EL2NRatio=5%2026.06 | — | — | 49.9 | |
| EVARatio=2%2026.06 | — | — | 53.5 | |
| EVARatio=5%2026.06 | — | — | 58.7 | |
| FacilityRatio=2%2026.06 | — | — | 77 | |
| FacilityRatio=5%2026.06 | — | — | 82.5 | |
| Forg.Ratio=2%2026.06 | — | — | 53.5 | |
| Forg.Ratio=5%2026.06 | — | — | 58.9 | |
| FPSRatio=2%2026.06 | — | — | 73 | |
| FPSRatio=5%2026.06 | — | — | 83 | |
| Graph Coverage SelectionRatio=2%2026.06 | — | — | 80.9 | |
| Graph Coverage SelectionRatio=5%2026.06 | — | — | 85.9 | |
| HerdingRatio=2%2026.06 | — | — | 78 | |
| HerdingRatio=5%2026.06 | — | — | 84.6 | |
| MoCoV3Backbone=ResNet-50, Initialization=random, Training Protocol=self-supervised2026.04 | — | 99.27 | — | |
| NNCLRBackbone=ResNet-50, Initialization=random, Training Protocol=self-supervised2026.04 | — | 99.26 | — | |
| RandomRatio=2%2026.06 | — | — | 77.5 | |
| RandomRatio=5%2026.06 | — | — | 84 | |
| ReSSLBackbone=ResNet-50, Initialization=random, Training Protocol=self-supervised2026.04 | — | 99.23 | — | |
| SimCLRBackbone=ResNet-50, Initialization=random, Training Protocol=self-supervised2026.04 | — | 99.45 | — | |
| VICRegBackbone=ResNet-50, Initialization=random, Training Protocol=self-supervised2026.04 | — | 99.25 | — |