WSI-level classification on Private-CRC (MV@5 Accuracy)
69MV@5 AccuracyPLIP
Evaluation Results
| Method | Links | |
|---|---|---|
| PLIPPre-training=Histopathology, Architecture=Transformer2023.11 | 69 | |
| MuDiPath-DenseNet-101Pre-training=Histopathology, Architecture=CNN-based2023.11 | 66 | |
| MoCoV2-ResNet50Pre-training=Histopathology, Architecture=CNN-based2023.11 | 65 | |
| BiomedCLIPPre-training=Histopathology, Architecture=Transformer2023.11 | 65 | |
| PathDino-512Pre-training=Histopathology, Architecture=Transformer, Input Resolution=5122023.11 | 65 | |
| EfficientNet-b5Pre-training=Natural, Architecture=CNN-based2023.11 | 64 | |
| Barlow-Twins-ResNet50Pre-training=Histopathology, Architecture=CNN-based2023.11 | 64 | |
| ResNet50Pre-training=Natural, Architecture=CNN-based2023.11 | 63 | |
| ConvNext-xlargePre-training=Natural, Architecture=CNN-based2023.11 | 63 | |
| iBOT-PathPre-training=Histopathology, Architecture=Transformer2023.11 | 63 | |
| DinoSSLPathology-8Pre-training=Histopathology, Architecture=Transformer2023.11 | 63 | |
| DinoV1-ViT-b16Pre-training=Natural, Architecture=Transformer2023.11 | 62 | |
| KimiaNetPre-training=Histopathology, Architecture=CNN-based2023.11 | 62 | |
| DinoV1-ViT-s16Pre-training=Natural, Architecture=Transformer2023.11 | 60 | |
| CLIP - ViT-B/16Pre-training=Natural, Architecture=Transformer2023.11 | 60 | |
| EfficientNet-b3-288Pre-training=Natural, Architecture=CNN-based2023.11 | 59 | |
| DinoV2-ViT-b14Pre-training=Natural, Architecture=Transformer2023.11 | 59 | |
| ConvNext-b-224Pre-training=Natural, Architecture=CNN-based2023.11 | 58 | |
| MuDiPath-ResNet50Pre-training=Histopathology, Architecture=CNN-based2023.11 | 57 | |
| PathDino-224Pre-training=Histopathology, Architecture=Transformer, Input Resolution=2242023.11 | 57 | |
| HIPT-ViT-s16Pre-training=Histopathology, Architecture=Transformer2023.11 | 55 | |
| ViT-b16-224Pre-training=Natural, Architecture=Transformer2023.11 | 54 | |
| DenseNet121Pre-training=Natural, Architecture=CNN-based2023.11 | 49 |