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---
datasets:
- Elsafty
- Chula
- DSE
library_name: timm
license: cc-by-4.0
pipeline_tag: image-feature-extraction
tags:
- red-blood-cells
- hematology
- medical-imaging
- vision-transformer
- dino
- dinov2
- feature-extraction
- foundation-model
model-index:
- name: RedDino-base
results:
- task:
type: image-classification
name: RBC Shape Classification
dataset:
name: Elsafty
type: Classification
metrics:
- type: Weighted F1
value: 88.1
- type: Balanced Accuracy
value: 89.3
- type: Accuracy
value: 88.2
- type: Weighted F1
value: 83.8
- type: Balanced Accuracy
value: 78.6
- type: Accuracy
value: 83.8
- type: Weighted F1
value: 85.9
- type: Balanced Accuracy
value: 57.9
- type: Accuracy
value: 86.0
---
# RedDino-base
**RedDino** is a self-supervised Vision Transformer foundation model specifically designed for **red blood cell (RBC)** image analysis.
It leverages a tailored version of the **DINOv2** framework, trained on a meticulously curated dataset of **1.25 million RBC images** from diverse acquisition modalities and sources.
This model excels at extracting robust, general-purpose features for downstream hematology tasks such as **shape classification**, **morphological subtype recognition**, and **batch-effect–robust analysis**.
Unlike general-purpose models pretrained on natural images, RedDino incorporates hematology-specific augmentations, architectural tweaks, and RBC-tailored data preprocessing, enabling **state-of-the-art performance** on multiple RBC benchmarks.
> 🧠 Developed by [Luca Zedda](https://orcid.org/0009-0001-8488-1612), [Andrea Loddo](https://orcid.org/0000-0002-6571-3816), [Cecilia Di Ruberto](https://orcid.org/0000-0003-4641-0307), and [Carsten Marr](https://orcid.org/0000-0003-2154-4552)
> 🏥 University of Cagliari & Helmholtz Munich
> 📄 Preprint: [arXiv:2508.08180](https://arxiv.org/abs/2508.08180)
> 💻 Code: [https://github.com/Snarci/RedDino](https://github.com/Snarci/RedDino)
---
## Model Details
- **Architecture:** ViT-base, patch size 14
- **SSL framework:** DINOv2 (customized for RBC morphology)
- **Pretraining dataset:** 1.25M RBC images from 18 datasets
- **Embedding size:** 768
- **Applications:** RBC morphology classification, feature extraction, batch-effect–robust analysis
## Example Usage
```python
from PIL import Image
from torchvision import transforms
import timm
import torch
# Load model from Hugging Face Hub
model = timm.create_model("hf_hub:Snarcy/RedDino-base", pretrained=True)
model.eval()
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
# Load and preprocess image
image = Image.open("path/to/rbc_image.jpg").convert("RGB")
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
input_tensor = transform(image).unsqueeze(0).to(device)
# Extract features
with torch.no_grad():
embedding = model(input_tensor)
```
## 📝 Citation
If you use this model, please cite the following paper:
**RedDino: A foundation model for red blood cell analysis**
Luca Zedda, Andrea Loddo, Cecilia Di Ruberto, Carsten Marr — 2025
Preprint: arXiv:2508.08180. https://arxiv.org/abs/2508.08180
```bibtex
@misc{zedda2025reddinofoundationmodelred,
title={RedDino: A foundation model for red blood cell analysis},
author={Luca Zedda and Andrea Loddo and Cecilia Di Ruberto and Carsten Marr},
year={2025},
eprint={2508.08180},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2508.08180},
}
``` |