TL;DR
RedDino is a self-supervised foundation model specifically designed for analyzing red blood cell images, improving classification accuracy and generalization in hematological diagnostics.
Contribution
It introduces a RBC-specific adaptation of DINOv2, providing a new foundation model for RBC analysis with extensive evaluation and ablation studies.
Findings
RedDino outperforms existing models in RBC shape classification.
It demonstrates strong feature representations and generalization ability.
The model captures nuanced morphological features for reliable diagnostics.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
