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 NOTICE from cloudkites/her2-reader: direct link, hf CLI and curl.
- Browser
- Download file 668 Bytes
-
https://huggingface.co/cloudkites/her2-reader/resolve/main/NOTICE
- Command line
-
hf download hf://cloudkites/her2-reader/NOTICE
-
curl -L -o NOTICE https://huggingface.co/cloudkites/her2-reader/resolve/main/NOTICE
668 Bytes
| This repository's encoders at stage 2 and later were trained by distillation from H-optimus-0 (Bioptimus), which is | |
| released under the Apache License, Version 2.0: https://www.apache.org/licenses/LICENSE-2.0 | |
| H-optimus-0 itself is not redistributed here. Our encoders are new weights (a ViT-S/16, 22M parameters) trained to match | |
| its outputs on TCGA tiles. | |
| Training and evaluation data: The Cancer Genome Atlas (NCI Genomic Data Commons, open access); CPTAC-BRCA (open); | |
| Yale-HER2 (The Cancer Imaging Archive, HER2-TUMOR-ROIS, CC BY 4.0). HEROHE (CC BY-NC-ND 3.0) and BCNB (non-commercial) | |
| were used for evaluation only, and no per-slide data from them are included. | |