基于WDLoRA的多模态生成框架用于临床指导的糖尿病性周围神经病变角膜共聚焦显微镜图像合成
A WDLoRA-Based Multimodal Generative Framework for Clinically Guided Corneal Confocal Microscopy Image Synthesis in Diabetic Neuropathy
AI总结：
本文提出基于WDLoRA的多模态生成框架，用于生成糖尿病性周围神经病变的角膜共聚焦显微镜图像，通过结合神经分割掩膜和疾病特异性临床提示，提升图像合成的解剖学一致性与临床诊断准确性。
摘要：
角膜共聚焦显微镜（CCM）是一种评估糖尿病周围神经病变（DPN）小纤维损伤的敏感工具，但开发稳健的自动深度学习诊断模型受限于标注数据稀缺和角膜神经形态的细粒度变异性。尽管人工智能驱动的基础生成模型在自然图像合成中表现优异，但它们在医学影像中往往因领域特定训练有限而难以满足临床分析所需的解剖学保真度。为此，我们提出了一种基于WDLoRA的多模态生成框架，用于临床指导的CCM图像合成。WDLoRA是一种参数高效微调（PEFT）机制，能够解耦幅度和方向权重更新，使基础生成模型能够独立学习方向（神经拓扑）和强度（基质对比度），以实现医学真实感。通过联合条件于神经分割掩膜和疾病特异性临床提示，模型在DPN谱（对照、T1无DPN、T1DPN）上生成解剖学一致的图像。全面的三支柱评估表明，所提出的框架在视觉保真度（FID：5.18）和结构完整性（SSIM：0.630）方面达到最先进的水平，显著优于GAN和标准扩散基线。关键的是，合成图像保留了金标准的临床生物标志物，并与真实患者数据在统计上等价。当用于训练自动诊断模型时，合成数据集提高了下游诊断准确性2.1%和分割性能2.2%，验证了该框架在缓解医学AI数据瓶颈方面的潜力。
英文摘要：
Corneal Confocal Microscopy (CCM) is a sensitive tool for assessing small-fiber damage in Diabetic Peripheral Neuropathy (DPN), yet the development of robust, automated deep learning-based diagnostic models is limited by scarce labelled data and fine-grained variability in corneal nerve morphology. Although Artificial Intelligence (AI)-driven foundation generative models excel at natural image synthesis, they often struggle in medical imaging due to limited domain-specific training, compromising the anatomical fidelity required for clinical analysis. To overcome these limitations, we propose a Weight-Decomposed Low-Rank Adaptation (WDLoRA)-based multimodal generative framework for clinically guided CCM image synthesis. WDLoRA is a parameter-efficient fine-tuning (PEFT) mechanism that decouples magnitude and directional weight updates, enabling foundation generative models to independently learn the orientation (nerve topology) and intensity (stromal contrast) required for medical realism. By jointly conditioning on nerve segmentation masks and disease-specific clinical prompts, the model synthesises anatomically coherent images across the DPN spectrum (Control, T1NoDPN, T1DPN). A comprehensive three-pillar evaluation demonstrates that the proposed framework achieves state-of-the-art visual fidelity (Fréchet Inception Distance (FID): 5.18) and structural integrity (Structural Similarity Index Measure (SSIM): 0.630), significantly outperforming GAN and standard diffusion baselines. Crucially, the synthetic images preserve gold-standard clinical biomarkers and are statistically equivalent to real patient data. When used to train automated diagnostic models, the synthetic dataset improves downstream diagnostic accuracy by 2.1% and segmentation performance by 2.2%, validating the framework's potential to alleviate data bottlenecks in medical AI.
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