metadata
license: apache-2.0
language:
- en
tags:
- cytopathology
- pathology
- medical-imaging
- foundation-model
- self-supervised-learning
- vision-transformer
library_name: pytorch
pipeline_tag: image-feature-extraction
CROWN
A Universal Visual Foundation Model for Computational Cytopathology.
CROWN is a visual foundation model pretrained on over 10 million cytology images.
It provides transferable and annotation-free feature representations for a wide range of cytological image analysis tasks.
Highlights
- Pretrained with a DINOv2-style self-supervised framework on large-scale cytology data
- Strong transferability across classification, retrieval, segmentation, detection, and slide-level weakly supervised tasks
- Robust under significant domain shifts, serving as a single scalable backbone for cytology research
Quick usage
After downloading our model weights：
from models.vision_transformer import vit_large
import torch
model = vit_large(
patch_size=16,
img_size=224,
init_values=1.0,
block_chunks=4,
ffn_layer="swiglufused",
)
state_dict = torch.load("CROWN.pth", map_location="cpu")
model.load_state_dict(state_dict, strict=True)
model.eval()
Used for feature extraction:
feat = model.forward_features(img)["x_norm_clstoken"]
Citation
If you find our work useful in your research or if you use parts of this code please consider citing our paper: