Towards Large-Scale Training of Pathology Foundation Models

This repository contains the official implementation of the research paper: "Towards Large-Scale Training of Pathology Foundation Models"

Pretrained models

Pre-trained backbones (via PyTorch Hub)

Use the code below to get started with the models:

# pip install timm
import torch

vits16 = torch.hub.load("kaiko-ai/towards_large_pathology_fms", "vits16", trust_repo=True)
vits8 = torch.hub.load("kaiko-ai/towards_large_pathology_fms", "vits8", trust_repo=True)
vitb16 = torch.hub.load("kaiko-ai/towards_large_pathology_fms", "vitb16", trust_repo=True)
vitb8 = torch.hub.load("kaiko-ai/towards_large_pathology_fms", "vitb8", trust_repo=True)
vitl14 = torch.hub.load("kaiko-ai/towards_large_pathology_fms", "vitl14", trust_repo=True)

Here is an end-to-end example:

# pip install timm
import io

import requests
import torch
from PIL import Image
from torchvision.transforms import v2

IMAGE_URL = "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQc7_xZpGOfQT7sxKwf2w5lL4GAq6IX_CbTzP1NGeenzA&s"
"""A sample WSI patch."""

# initialize the model pre-process transforms
preprocessing = v2.Compose(
    [
        v2.ToImage(),
        v2.Resize(size=224),
        v2.CenterCrop(size=224),
        v2.ToDtype(torch.float32, scale=True),
        v2.Normalize(
            mean=(0.5, 0.5, 0.5),
            std=(0.5, 0.5, 0.5),
        ),
    ]
)

# initialize the vision FM model
model = torch.hub.load("kaiko-ai/towards_large_pathology_fms", "vits16", trust_repo=True)
model.eval()

# perform model forward pass and get the feature embeddings
image = Image.open(io.BytesIO(requests.get(IMAGE_URL).content))
image_tensor = preprocessing(image)
features = model(image_tensor.unsqueeze(0))
assert features.shape == torch.Size([1, 384])

Citation

If you find this repository useful, please consider giving a star ⭐ and adding the following citation:

@misc{ai2024largescale,
    title={Towards Large-Scale Training of Pathology Foundation Models}, 
    author={kaiko.ai and Nanne Aben and Edwin D. de Jong and Ioannis Gatopoulos and Nicolas Känzig and Mikhail Karasikov and Axel Lagré and Roman Moser and Joost van Doorn and Fei Tang},
    year={2024},
    eprint={2404.15217},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}