RedDino是首个专用于红细胞分析的自监督基础模型，可精准识别形态异常。
RedDino: A foundation model for red blood cell analysis
- 基于DINOv2框架定制红细胞专用自监督学习
- 在125万张红细胞图像上训练，分类性能超越现有模型
- 适合血液病诊断研究者与医学影像算法开发人员
红细胞（RBC）对人类健康至关重要，其形态精确分析有助于血液疾病诊断。尽管基础模型在医疗诊断中前景广阔，但针对RBC分析的完整AI解决方案仍十分稀缺。本文提出RedDino，一种专为红细胞图像分析设计的自监督基础模型。该模型采用改进版DINOv2框架，在涵盖多种成像方式与来源的125万张红细胞图像数据集上进行训练。大量实验表明，RedDino在红细胞形状分类任务上优于现有最先进模型。通过线性探测和最近邻分类评估，验证了其强大的特征表示能力与泛化性能。主要贡献包括：（1）面向红细胞分析的基础模型；（2）针对红细胞建模的DINOv2配置消融研究；（3）全面的泛化性能评估。RedDino通过捕捉细微形态特征，解决了计算血液学中的关键挑战，推动可靠诊断工具的发展。源代码与预训练模型已开源，可通过GitHub与Hugging Face获取。
原文摘要 · Abstract (English)
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
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