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RedDino: A Foundation Model for Red Blood Cell Analysis.
In: (28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025, 23-27 September 2025, Daejeon). Berlin [u.a.]: Springer, 2026. 445-455 (Lect. Notes Comput. Sc. ; 15963 LNCS)
Red blood cells (RBCs) are fundamental to human health, and precise morphological analysis is critical for diagnosing hematological disorders. Despite the potential of foundation models for medical diagnostics, comprehensive AI solutions for RBC analysis remain limited. We introduce RedDino, a self-supervised foundation model specifically designed for RBC image analysis. Leveraging a RBC-tailored version of the DINOv2 self-supervised learning framework, RedDino is trained on an extensive, meticulously curated dataset comprising over 1.25 million RBC images from diverse acquisition modalities and sources. Comprehensive evaluations demonstrate that RedDino significantly outperforms existing state-of-the-art models in the RBC shape classification. Through systematic assessments, including linear probing and nearest neighbor classification, we validate the model’s robust feature representation and strong generalization capabilities. Our key contributions are (1) a dedicated foundation model tailored for RBC analysis, (2) detailed ablation studies exploring DINOv2 configurations for RBC modeling, and (3) comprehensive generalization performance evaluation. RedDino captures nuanced morphological characteristics and represents a substantial advancement in developing reliable diagnostic tools. Source code and pretrained models for RedDino are available at https://github.com/Snarci/RedDino.
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Publication type
Article: Conference contribution
Keywords
Dinov2 ; Foundation Models ; Hematology ; Medical Imaging ; Red Blood Cell Analysis ; Self-supervised Learning
Language
english
Publication Year
2026
HGF-reported in Year
2026
ISSN (print) / ISBN
0302-9743
e-ISSN
1611-3349
Conference Title
28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025
Conference Date
23-27 September 2025
Conference Location
Daejeon
Quellenangaben
Volume: 15963 LNCS,
Pages: 445-455
Publisher
Springer
Publishing Place
Berlin [u.a.]
Institute(s)
Institute of AI for Health (AIH)
POF-Topic(s)
30205 - Bioengineering and Digital Health
Research field(s)
Enabling and Novel Technologies
PSP Element(s)
G-540007-001
Scopus ID
105017852001
Erfassungsdatum
2025-10-23