RedDino: A foundation model for red blood cell analysis
Red blood cells (RBCs) are essential to human health, and their precise
morphological analysis is important for diagnosing hematological disorders.
Despite the promise of foundation models in medical diagnostics, comprehensive
AI solutions for RBC analysis remain scarce. We present RedDino, a
self-supervised foundation model designed for RBC image analysis. RedDino uses
an RBC-specific adaptation of the DINOv2 self-supervised learning framework and
is trained on a curated dataset of 1.25 million RBC images from diverse
acquisition modalities and sources. Extensive evaluations show that RedDino
outperforms existing state-of-the-art models on RBC shape classification.
Through assessments including linear probing and nearest neighbor
classification, we confirm its strong feature representations and
generalization ability. Our main contributions are: (1) a foundation model
tailored for RBC analysis, (2) ablation studies exploring DINOv2 configurations
for RBC modeling, and (3) a detailed evaluation of generalization performance.
RedDino addresses key challenges in computational hematology by capturing
nuanced morphological features, advancing the development of reliable
diagnostic tools. The source code and pretrained models for RedDino are
available at https://github.com/Snarci/RedDino, and the pretrained models can
be downloaded from our Hugging Face collection at
https://huggingface.co/collections/Snarcy/reddino-689a13e29241d2e5690202fc
689ade29fab6fdd2e52ac565
Luca Zedda
claimed_verified
2025-08-13T07:18:59.626Z
67bed13027ea9754039d63c3
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/JHlZXuwcY0y_estgKPkPT.jpeg
Luca Zedda
user
Snarcy
689ade29fab6fdd2e52ac566
Andrea Loddo
689ade29fab6fdd2e52ac567
Cecilia Di Ruberto
689ade29fab6fdd2e52ac568
Carsten Marr
RedDino: A foundation model for red blood cell analysis
Red blood cells (RBCs) are essential to human health, and their precise
morphological analysis is important for diagnosing hematological disorders.
Despite the promise of foundation models in medical diagnostics, comprehensive
AI solutions for RBC analysis remain scarce. We present RedDino, a
self-supervised foundation model designed for RBC image analysis. RedDino uses
an RBC-specific adaptation of the DINOv2 self-supervised learning framework and
is trained on a curated dataset of 1.25 million RBC images from diverse
acquisition modalities and sources. Extensive evaluations show that RedDino
outperforms existing state-of-the-art models on RBC shape classification.
Through assessments including linear probing and nearest neighbor
classification, we confirm its strong feature representations and
generalization ability. Our main contributions are: (1) a foundation model
tailored for RBC analysis, (2) ablation studies exploring DINOv2 configurations
for RBC modeling, and (3) a detailed evaluation of generalization performance.
RedDino addresses key challenges in computational hematology by capturing
nuanced morphological features, advancing the development of reliable
diagnostic tools. The source code and pretrained models for RedDino are
available at https://github.com/Snarci/RedDino, and the pretrained models can
be downloaded from our Hugging Face collection at
https://huggingface.co/collections/Snarcy/reddino-689a13e29241d2e5690202fc
RedDino, a self-supervised foundation model using DINOv2, excels in RBC shape classification and generalization, addressing challenges in computational hematology.
self-supervised foundation model DINOv2 RBC image analysis RBC shape classification linear probing nearest neighbor classification feature representations generalization ability computational hematology