---
license: other
license_name: exaonepath
license_link: LICENSE
tags:
- lg-ai
- EXAONEPath-1.0
- pathology
- lg-ai
---
# EXAONEPath
## EXAONEPath 1.0 Patch-level Foundation Model for Pathology
[[`Paper`](https://arxiv.org/abs/2408.00380)] [[`Github`](https://github.com/LG-AI-EXAONE/EXAONEPath)] [[`Model`](https://github.com/LG-AI-EXAONE/EXAONEPath/releases/download/1.0.0/EXAONEPath.ckpt)] [[`BibTeX`](#Citation)]
## Introduction
We introduce EXAONEPath, a patch-level pathology pretrained model with 86 million parameters.
The model was pretrained on 285,153,903 patches extracted from a total of 34,795 WSIs.
EXAONEPath demonstrates superior performance considering the number of WSIs used and the model's parameter count.
## Quickstart
Load EXAONEPath and run inference on tile-level images.
### 1. Hardware Requirements ###
- NVIDIA GPU is required
- Minimum 8GB GPU memory recommended
- NVIDIA driver version >= 450.80.02 required
Note: This implementation requires NVIDIA GPU and drivers. The provided environment setup specifically uses CUDA-enabled PyTorch, making NVIDIA GPU mandatory for running the model.
### 2. Environment Setup ###
First, install Conda if you haven't already. You can find installation instructions [here](https://docs.anaconda.com/miniconda/).
Then create and activate the environment using the provided configuration:
```bash
git clone https://github.com/LG-AI-EXAONE/EXAONEPath.git
cd EXAONEPath
conda env create -f environment.yaml
conda activate exaonepath
```
### 3. Load the model & Inference
#### Load with HuggingFace
```python
import torch
from PIL import Image
from macenko import macenko_normalizer
import torchvision.transforms as transforms
from vision_transformer import VisionTransformer
hf_token = "YOUR_HUGGING_FACE_ACCESS_TOKEN"
model = VisionTransformer.from_pretrained("LGAI-EXAONE/EXAONEPath", use_auth_token=hf_token)
transform = transforms.Compose(
[
transforms.Resize(256, interpolation=transforms.InterpolationMode.BICUBIC),
transforms.CenterCrop(224),
transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
]
)
normalizer = macenko_normalizer()
img_path = "images/MHIST_aaa.png"
image = Image.open(img_path).convert("RGB")
image_macenko = normalizer(image)
sample_input = transform(image_macenko).unsqueeze(0)
model.cuda()
model.eval()
features = model(sample_input.cuda())
```
#### Load Manually
First, download the EXAONEPath model checkpoint from [here](https://github.com/LG-AI-EXAONE/EXAONEPath/releases/download/1.0.0/EXAONEPath.ckpt)
```python
import torch
from PIL import Image
from macenko import macenko_normalizer
import torchvision.transforms as transforms
from vision_transformer import vit_base
file_path = "MODEL_CHECKPOINT_PATH"
checkpoint = torch.load(file_path, map_location=torch.device('cpu'))
state_dict = checkpoint['state_dict']
model = vit_base(patch_size=16, num_classes=0)
msg = model.load_state_dict(state_dict, strict=False)
print(f'Pretrained weights found at {file_path} and loaded with msg: {msg}')
transform = transforms.Compose(
[
transforms.Resize(256, interpolation=transforms.InterpolationMode.BICUBIC),
transforms.CenterCrop(224),
transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
]
)
normalizer = macenko_normalizer()
img_path = "images/MHIST_aaa.png"
image = Image.open(img_path).convert("RGB")
image_macenko = normalizer(image)
sample_input = transform(image_macenko).unsqueeze(0)
model.cuda()
model.eval()
features = model(sample_input.cuda())
```
## Model Performance Comparison
We report linear evaluation result on six downstream tasks. Top-1 accuracy is shown, with values for models other than Gigapath taken from the RudolfV paper.
| Model | PCAM | MHIST | CRC-100K | TIL Det. | MSI CRC | MSI STAD | Avg |
|--------------------------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|
| ResNet50 ImageNet | 0.833 | 0.806 | 0.849 | 0.915 | 0.653 | 0.664 | 0.787 |
| ViT-L/16 ImageNet | 0.852 | 0.796 | 0.847 | 0.924 | 0.669 | 0.671 | 0.793 |
| Lunit | 0.918 | 0.771 | 0.949 | 0.943 | 0.745 | 0.756 | 0.847 |
| CTransPath | 0.872 | 0.817 | 0.840 | 0.930 | 0.694 | 0.726 | 0.813 |
| Phikon | 0.906 | 0.795 | 0.883 | **0.946** | 0.733 | 0.751 | 0.836 |
| Virchow | 0.933 | **0.834** | 0.968 | - | - | - | - |
| RudolfV | 0.944 | 0.821 | **0.973** | 0.943 | 0.755 | 0.788 | **0.871** |
| GigaPath (patch encoder) | **0.947** | 0.822 | 0.964 | 0.938 | 0.753 | 0.748 | 0.862 |
| EXAONEPath (ours) | 0.901 | 0.818 | 0.946 | 0.939 | **0.756** | **0.804** | 0.861 |
Figure 1. Performance comparison of models based on the number of parameters and the number of WSIs used for training. The average Top-1 accuracy represents the mean linear evaluation performance across six downstream tasks.
## License
The model is licensed under [EXAONEPath AI Model License Agreement 1.0 - NC](./LICENSE)
## Citation
If you find EXAONEPath useful, please cite it using this BibTeX:
```
@article{yun2024exaonepath,
title={EXAONEPath 1.0 Patch-level Foundation Model for Pathology},
author={Yun, Juseung and Hu, Yi and Kim, Jinhyung and Jang, Jongseong and Lee, Soonyoung},
journal={arXiv preprint arXiv:2408.00380},
year={2024}
}
```
## Contact
LG AI Research Technical Support: contact_us1@lgresearch.ai
