---
license: mit
---

# Checkpoint for Multistain Pretraining for Slide Representation Learning in Pathology (ECCV'24)

Welcome to the official HuggingFace repository of the ECCV 2024 paper, ["Multistain Pretraining for Slide Representation Learning in Pathology"](https://huggingface.co/papers/2408.02859).
This project was developed at the [Mahmood Lab](https://faisal.ai/) at Harvard Medical School and Brigham and Women's Hospital.

## Model loging

```
from huggingface_hub import notebook_login
notebook_login()
```

You can refer [HuggingFace](https://huggingface.co/docs/huggingface_hub/en/quick-start#login-command) documentation for more details.

## Preprocessing: tissue segmentation, patching, and patch feature extraction

We are extracting [CONCH](https://github.com/mahmoodlab/CONCH) features at 10x magnification on 256x256-pixel patches. Please refer to Madeleine public implementation to extract patch embeddings from a WSI.

## Extracting MADELEINE slide encoding

You can obtain and run MADELEINE slide encoding (trained on Acrobat breast samples at 10x magnification) using:

```
from core.models.factory import create_model_from_pretrained

model, precision = create_model_from_pretrained('./models/')

feats = load_h5('your_path_to_conch_patch_embeddings/XXX.h5')

with torch.no_grad():
  with torch.amp.autocast(device_type="cuda", dtype=precision):
      wsi_embed = model.encode_he(feats=feats, device='cuda')
```