用医生诊断思路融合影像与病历，提升不明原因发热早期诊断准确率
MedMimic: Physician-Inspired Multimodal Fusion for Early Diagnosis of Fever of Unknown Origin
- 模仿临床诊断流程，用自注意力融合影像与临床数据
- 在416例患者上实现0.865至0.929的宏平均AUROC
- 适合医学AI、多模态诊断方向的研究者参考
不明原因发热（FUO）仍是诊断难题。本文提出受真实诊疗流程启发的多模态框架MedMimic，利用DINOv2、Vision Transformer和ResNet-18等预训练模型，将高维18F-FDG PET/CT影像转化为低维语义特征。再通过可学习的自注意力融合网络，整合影像特征与临床数据进行分类。基于四川大学华西医院2017至2023年416例FUO患者数据，该多模态融合分类网络（MFCN）在七个任务中达到0.8654至0.9291的宏平均AUROC，优于传统机器学习及单模态深度学习方法。消融实验与五折交叉验证进一步验证其有效性。通过结合预训练大模型与深度学习优势，MedMimic为疾病分类提供了一种有前景的解决方案。
原文摘要 · Abstract (English)
Fever of unknown origin FUO remains a diagnostic challenge. MedMimic is introduced as a multimodal framework inspired by real-world diagnostic processes. It uses pretrained models such as DINOv2, Vision Transformer, and ResNet-18 to convert high-dimensional 18F-FDG PET/CT imaging into low-dimensional, semantically meaningful features. A learnable self-attention-based fusion network then integrates these imaging features with clinical data for classification. Using 416 FUO patient cases from Sichuan University West China Hospital from 2017 to 2023, the multimodal fusion classification network MFCN achieved macro-AUROC scores ranging from 0.8654 to 0.9291 across seven tasks, outperforming conventional machine learning and single-modality deep learning methods. Ablation studies and five-fold cross-validation further validated its effectiveness. By combining the strengths of pretrained large models and deep learning, MedMimic offers a promising solution for disease classification.
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