Image Feature Extraction
timm
Safetensors
pathology
histopathology
her2
breast-cancer
h-and-e
vision-transformer
multiple-instance-learning
Instructions to use cloudkites/her2-reader with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use cloudkites/her2-reader with timm:
import timm model = timm.create_model("hf-hub:cloudkites/her2-reader", pretrained=True) - Notebooks
- Google Colab
- Kaggle
File size: 966 Bytes
4367928 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | % Generated by docs/paper/paper_pathbench.py (local PathBench, HER2 status, 10 paired seeds)
\begin{tabularx}{\linewidth}{@{}>{\raggedright\arraybackslash}X l r cc cc@{}}
\toprule
& & & \multicolumn{2}{c}{TCGA-BRCA} & \multicolumn{2}{c}{Yale-HER2} \\
\cmidrule(lr){4-5}\cmidrule(lr){6-7}
tile encoder & architecture & params & AUC & $p$ vs best & AUC & $p$ vs best \\
\midrule
\textbf{Ours (final)} & ViT-S/16 & 22M & \textbf{0.689$^{1}$} & -- & \textbf{0.897$^{3}$} & 0.16 \\
H-optimus-0 & ViT-g/14 & 1.1B & 0.660$^{2}$ & 0.08 & 0.913$^{1}$ & -- \\
Phikon & ViT-B/16 & 86M & 0.656$^{3}$ & 0.18 & 0.901$^{2}$ & 0.18 \\
Midnight-12k & ViT-g/14 & 1.1B & 0.630$^{4}$ & $<$0.01 & 0.878$^{4}$ & 0.02 \\
Hibou-B & ViT-B/14 & 86M & 0.605$^{5}$ & $<$0.01 & 0.853$^{5}$ & $<$0.01 \\
DINOv2-S$^\ast$ & ViT-S/14 & 22M & 0.541$^{7}$ & $<$0.01 & 0.794$^{6}$ & $<$0.01 \\
ResNet-50$^\ast$ & CNN & 26M & 0.567$^{6}$ & $<$0.01 & 0.730$^{7}$ & $<$0.01 \\
\bottomrule
\end{tabularx}
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