README.md

metadata

tags:
  - timm
  - feature-extraction
  - image-classification
library_name: timm
license: other
license_name: kaiko-non-commercial
license_link: >-
  https://github.com/kaiko-ai/towards_large_pathology_fms/blob/a62a0c54719d858371aefa0fcab6ec4b34c86c4c/LICENSE
metrics:
  - accuracy
model-index:
  - name: kaiko
    results:
      - task:
          type: image-classification
          name: Image Classification
        dataset:
          name: BACH
          type: image-classification
        metrics:
          - type: accuracy
            value: 0.81
            name: Accuracy
            verified: false
      - task:
          type: image-classification
          name: Image Classification
        dataset:
          name: CRC-NCT-HE
          type: image-classification
        metrics:
          - type: accuracy
            value: 0.96
            name: Accuracy
            verified: false
      - task:
          type: image-classification
          name: Image Classification
        dataset:
          name: MHIST
          type: image-classification
        metrics:
          - type: accuracy
            value: 0.826
            name: Accuracy
            verified: false
      - task:
          type: image-classification
          name: Image Classification
        dataset:
          name: PCam
          type: image-classification
        metrics:
          - type: accuracy
            value: 0.898
            name: Accuracy
            verified: false
      - task:
          type: image-classification
          name: Image Classification
        dataset:
          name: TP53
          type: image-classification
        metrics:
          - type: accuracy
            value: 0.651
            name: Accuracy
            verified: false
      - task:
          type: image-classification
          name: Image Classification
        dataset:
          name: CoNSeP
          type: image-classification
        metrics:
          - type: accuracy
            value: 0.658
            name: Accuracy
            verified: false

Model card for vit_base_patch16_224.kaiko_ai_towards_large_pathology_fms

Model Details

• Model Type: Feature backbone
• Model Stats:
  • Params: 86M (base)
  • Image size: 224 x 224 x 3
  • Patch size: 16 x 16 x 3
• Repository: github.com:kaiko-ai/towards_large_pathology_fms
• Original Weights: github.com:kaiko-ai/towards_large_pathology_fms/0.0.1
• Papers:
  • Towards Large-Scale Training of Pathology Foundation Models

Model Usage

Image Embeddings

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

# get example histology image
url = "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQc7_xZpGOfQT7sxKwf2w5lL4GAq6IX_CbTzP1NGeenzA&s"
image = Image.open(io.BytesIO(requests.get(url).content))

# load model from the hub
model = timm.create_model(
  model_name="hf-hub:1aurent/vit_base_patch16_224.kaiko_ai_towards_large_pathology_fms",
  dynamic_img_size=True,
  pretrained=True,
).eval()

# get image transform
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),
    ),
  ]
)

data = preprocessing(image).unsqueeze(0) # input is a (batch_size, num_channels, img_size, img_size) shaped tensor
output = model(data)  # output is a (batch_size, num_features) shaped tensor

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}
}