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--- |
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license: cc-by-4.0 |
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tags: |
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- red-blood-cells |
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- hematology |
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- medical-imaging |
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- vision-transformer |
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- dino |
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- dinov2 |
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- feature-extraction |
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- foundation-model |
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library_name: timm |
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datasets: |
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- Elsafty |
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- Chula |
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- DSE |
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pipeline_tag: feature-extraction |
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model-index: |
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- name: RedDino-large |
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results: |
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- task: |
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type: image-classification |
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name: RBC Shape Classification |
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dataset: |
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name: Elsafty |
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type: Classification |
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metrics: |
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- type: Weighted F1 |
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value: 88.5 |
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- type: Balanced Accuracy |
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value: 89.1 |
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- type: Accuracy |
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value: 88.4 |
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- task: |
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type: image-classification |
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name: RBC Shape Classification |
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dataset: |
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name: Chula |
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type: Classification |
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metrics: |
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- type: Weighted F1 |
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value: 83.9 |
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- type: Balanced Accuracy |
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value: 79.0 |
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- type: Accuracy |
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value: 85.0 |
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- task: |
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type: image-classification |
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name: RBC Shape Classification |
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dataset: |
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name: DSE |
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type: Classification |
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metrics: |
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- type: Weighted F1 |
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value: 86.6 |
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- type: Balanced Accuracy |
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value: 60.1 |
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- type: Accuracy |
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value: 86.6 |
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--- |
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# RedDino-large |
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**RedDino** is a self-supervised Vision Transformer foundation model specifically designed for **red blood cell (RBC)** image analysis. |
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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. |
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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**. |
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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. |
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> 🧠 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) |
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> 🏥 University of Cagliari & Helmholtz Munich |
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> 📄 Preprint: [arXiv:2508.08180](https://arxiv.org/abs/2508.08180) |
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--- |
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## Model Details |
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- **Architecture:** ViT-large, patch size 14 |
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- **SSL framework:** DINOv2 (customized for RBC morphology) |
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- **Pretraining dataset:** Curated RBC images from 18 datasets (multiple modalities and sources) |
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- **Embedding size:** 1024 |
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- **Intended use:** RBC morphology classification, feature extraction, batch-effect–robust analysis |
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Notes: |
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- RBC-specific training strategy including removal of KoLeo regularizer and Sinkhorn-Knopp centering. |
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- Training on smear patches (not only single cells) to enhance cross-source generalization. |
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## Example Usage |
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```python |
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from PIL import Image |
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from torchvision import transforms |
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import timm |
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import torch |
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# Load model from Hugging Face Hub |
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model = timm.create_model("hf_hub:Snarcy/RedDino-large", pretrained=True) |
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model.eval() |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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model.to(device) |
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# Load and preprocess image |
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image = Image.open("path/to/rbc_image.jpg").convert("RGB") |
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transform = transforms.Compose([ |
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transforms.Resize((224, 224)), |
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transforms.ToTensor(), |
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transforms.Normalize(mean=[0.485, 0.456, 0.406], |
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std=[0.229, 0.224, 0.225]), |
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]) |
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input_tensor = transform(image).unsqueeze(0).to(device) |
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# Extract features |
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with torch.no_grad(): |
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embedding = model(input_tensor) |
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``` |
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## 📝 Citation |
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If you use this model, please cite the following paper: |
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**RedDino: A foundation model for red blood cell analysis** |
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Luca Zedda, Andrea Loddo, Cecilia Di Ruberto, Carsten Marr — 2025 |
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Preprint: arXiv:2508.08180. https://arxiv.org/abs/2508.08180 |
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```bibtex |
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@misc{zedda2025reddinofoundationmodelred, |
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title={RedDino: A foundation model for red blood cell analysis}, |
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author={Luca Zedda and Andrea Loddo and Cecilia Di Ruberto and Carsten Marr}, |
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year={2025}, |
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eprint={2508.08180}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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url={https://arxiv.org/abs/2508.08180}, |
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} |
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``` |
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