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
Download data/paper/paper-data/table_her2tasks.tex from cloudkites/her2-reader: direct link, hf CLI and curl.
- Browser
- Download file 893 Bytes
-
https://huggingface.co/cloudkites/her2-reader/resolve/main/data/paper/paper-data/table_her2tasks.tex
- Command line
-
hf download hf://cloudkites/her2-reader/data/paper/paper-data/table_her2tasks.tex
-
curl -L -o table_her2tasks.tex https://huggingface.co/cloudkites/her2-reader/resolve/main/data/paper/paper-data/table_her2tasks.tex
893 Bytes
| % Generated by docs/paper/paper_her2tasks.py (TCGA-BRCA; 10 paired seeds) | |
| \begin{tabularx}{\linewidth}{@{}>{\raggedright\arraybackslash}X r c cc@{}} | |
| \toprule | |
| & & HER2-low vs HER2-zero & \multicolumn{2}{c}{FISH amplification, read by the HER2 reader} \\ | |
| \cmidrule(lr){3-3}\cmidrule(lr){4-5} | |
| tile encoder & params & dedicated head & Yale-trained & TCGA-trained, out of fold \\ | |
| \midrule | |
| Phikon & 86M & 0.741$^{1}$ & 0.619$^{6}$ & 0.654$^{3}$ \\ | |
| H-optimus-0 & 1.1B & 0.692$^{2}$ & 0.706$^{1}$ & 0.690$^{1}$ \\ | |
| Hibou-B & 86M & 0.678$^{3}$ & 0.672$^{3}$ & 0.572$^{5}$ \\ | |
| \textbf{Ours (final)} & 22M & \textbf{0.649$^{4}$} & \textbf{0.682$^{2}$} & \textbf{0.669$^{2}$} \\ | |
| Midnight-12k & 1.1B & 0.626$^{5}$ & 0.650$^{4}$ & 0.644$^{4}$ \\ | |
| ResNet-50$^\ast$ & 26M & 0.615$^{6}$ & 0.575$^{7}$ & 0.445$^{7}$ \\ | |
| DINOv2-S$^\ast$ & 22M & 0.613$^{7}$ & 0.632$^{5}$ & 0.469$^{6}$ \\ | |
| \bottomrule | |
| \end{tabularx} | |