用AI生成高质量血细胞图像，解决医学数据少且不平衡的问题
CytoDiff: AI-Driven Cytomorphology Image Synthesis for Medical Diagnostics
- 用微调的扩散模型加少量样本引导生成逼真血细胞图
- 每类加5000张合成图，分类准确率从27%提至78%
- 适合医疗影像数据稀缺场景，保护隐私又提升模型性能
生物医学数据常受严格隐私限制，且存在严重类别不平衡，阻碍机器学习模型的准确训练。生成式AI虽有潜力，但生成足够质量的合成图像仍具挑战。本文聚焦于白血细胞分类，这是诊断急性髓系白血病等血液恶性肿瘤的关键任务。我们提出CytoDiff，一种通过LoRA权重微调的稳定扩散模型，结合少量样本引导生成高保真白血细胞图像。在小规模、高度不平衡的真实数据集上，每类添加5000张合成图像后，ResNet分类器准确率从27%提升至78%（+51%）；基于CLIP的分类准确率则从62%升至77%（+15%）。结果表明，合成图像生成是生物医学机器学习的有效工具，可增强数据覆盖、促进安全数据共享并保护患者隐私。论文代码已公开于https://github.com/JanCarreras24/CytoDiff。
原文摘要 · Abstract (English)
Biomedical datasets are often constrained by stringent privacy requirements and frequently suffer from severe class imbalance. These two aspects hinder the development of accurate machine learning models. While generative AI offers a promising solution, producing synthetic images of sufficient quality for training robust classifiers remains challenging. This work addresses the classification of individual white blood cells, a critical task in diagnosing hematological malignancies such as acute myeloid leukemia (AML). We introduce CytoDiff, a stable diffusion model fine-tuned with LoRA weights and guided by few-shot samples that generates high-fidelity synthetic white blood cell images. Our approach demonstrates substantial improvements in classifier performance when training data is limited. Using a small, highly imbalanced real dataset, the addition of 5,000 synthetic images per class improved ResNet classifier accuracy from 27\% to 78\% (+51\%). Similarly, CLIP-based classification accuracy increased from 62\% to 77\% (+15\%). These results establish synthetic image generation as a valuable tool for biomedical machine learning, enhancing data coverage and facilitating secure data sharing while preserving patient privacy. Paper code is publicly available at https://github.com/JanCarreras24/CytoDiff.
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