Kavli Affiliate: Yi Zhou
| First 5 Authors: Minrui Chen, Yi Zhou, Huidong Jiang, Yuhan Zhu, Guanjie Zou
| Summary:
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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