RedDino Large

RedDino: A Foundation Model for Red Blood Cell Analysis

RedDino is a self-supervised Vision Transformer foundation model specifically designed for red blood cell (RBC) image analysis, as presented in the paper RedDino: A foundation model for red blood cell 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, Andrea Loddo, Cecilia Di Ruberto, and Carsten Marr
🏥 University of Cagliari & Helmholtz Munich
📄 Preprint: arXiv:2508.08180
💻 Code: https://github.com/Snarci/RedDino

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Model Details

• Architecture: ViT-large, patch size 14
• SSL framework: DINOv2 (customized for RBC morphology)
• Pretraining dataset: Curated RBC images from 18 datasets (multiple modalities and sources)
• Embedding size: 1024
• Intended use: RBC morphology classification, feature extraction, batch-effect–robust analysis Notes:
• RBC-specific training strategy including removal of KoLeo regularizer and Sinkhorn-Knopp centering.
• Training on smear patches (not only single cells) to enhance cross-source generalization.

Example Usage

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-large", 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)

Model Variants

RedDino comes in three sizes to suit different computational requirements and performance needs:

Model Variant	Embedding Size	Parameters	Usage
RedDino-small	384	22M	timm.create_model("hf_hub:Snarcy/RedDino-small", pretrained=True)
RedDino-base	768	86M	timm.create_model("hf_hub:Snarcy/RedDino-base", pretrained=True)
RedDino-large	1024	304M	timm.create_model("hf_hub:Snarcy/RedDino-large", pretrained=True)

Choose the variant that best fits your computational budget and performance requirements. Larger models generally provide richer feature representations at the cost of increased computational overhead.

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Benchmark Results

RedDino was benchmarked on major RBC classification datasets—including Elsafty, Chula, and DSE—outperforming state-of-the-art baselines such as ResNet50, DinoBloom, and DINOv2.

Model	Dataset	Metric	Linear Probing (wF1)	1-NN (wF1)	20-NN (wF1)
ResNet50	Elsafty	Weighted F1	77.6 ± 8.1	64.3 ± 4.8	66.2 ± 4.9
DinoBloom-S	Elsafty	Weighted F1	83.2 ± 8.2	73.1 ± 5.1	76.5 ± 4.2
DINOv2 (small)	Elsafty	Weighted F1	82.1 ± 8.2	73.5 ± 4.8	77.2 ± 4.6
RedDino small	Elsafty	Weighted F1	86.0 ± 7.0	76.8 ± 4.9	80.0 ± 4.5
RedDino base	Elsafty	Weighted F1	88.1 ± 4.9	78.8 ± 3.6	82.6 ± 2.8
RedDino large	Elsafty	Weighted F1	88.5 ± 5.5	78.5 ± 4.6	81.6 ± 4.7

On Chula and DSE datasets, RedDino consistently surpassed all other models in feature quality (linear probing) with average improvements of 2–4% over prior approaches in key metrics.

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Highlights

• Foundation model for RBC analysis trained on the largest available multi-source RBC image set: 1.25M+ images, using advanced CellPose-based instance segmentation and patch extraction.
• DINOv2-based self-supervised learning for label-efficient pretraining and robust, transferable features.
• Model architecture and key innovations:
  • Patch-based training (224×224 px) shown to outperform single-cell training.
  • Novel data augmentation via Albumentations (32 pixel-level strategies).
  • Removal of the Koleo regularizer and adoption of Sinkhorn-Knopp centering for improved representation in RBC-specific domains.
  • Suite of models (small, base, large) covering 22M–304M parameters.
• Generalization: Strong adaptation across varied protocols, microscopes, and imaging sites. Demonstrated resistance to batch effects and out-of-domain variance.
• Interpretability tools: PCA/UMAP visualizations reveal clustering by phenotype and batch, distinguishing abnormal cells (e.g., malaria, echinocytes).
• Easy deployment: Models and code are available on GitHub and Hugging Face.

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📝 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

@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}, 
}

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Summary

RedDino is the first family of foundation models tailored for comprehensive red blood cell image analysis, using large-scale self-supervised learning to set new performance benchmarks and generalization standards for computational hematology. Models and pretrained weights are available for research and practical deployment.