MedMimic: Physician-Inspired Multimodal Fusion for Early Diagnosis of Fever of Unknown Origin
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arXiv
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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
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2025
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| author | Chen, Minrui Zhou, Yi Jiang, Huidong Zhu, Yuhan Zou, Guanjie Chen, Minqi Tian, Rong Saigo, Hiroto |
| author_facet | Chen, Minrui Zhou, Yi Jiang, Huidong Zhu, Yuhan Zou, Guanjie Chen, Minqi Tian, Rong Saigo, Hiroto |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_04794 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MedMimic: Physician-Inspired Multimodal Fusion for Early Diagnosis of Fever of Unknown Origin Chen, Minrui Zhou, Yi Jiang, Huidong Zhu, Yuhan Zou, Guanjie Chen, Minqi Tian, Rong Saigo, Hiroto Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition 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. |
| title | MedMimic: Physician-Inspired Multimodal Fusion for Early Diagnosis of Fever of Unknown Origin |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.04794 |