EXAONEPath

EXAONEPath 1.0 Patch-level Foundation Model for Pathology

[Paper] [Github] [Model] [BibTeX]

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. Install the requirements and the package

git clone https://github.com/LG-AI-EXAONE/EXAONEPath.git
cd EXAONEPath
pip install -r requirements.txt

2. Load the model & Inference

Load with HuggingFace

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

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

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: [email protected]