弱监督下融合多模态医学数据，提升影像诊断模型性能
Weakly-Supervised Multimodal Learning on MIMIC-CXR
- 用变分混合专家模型整合影像与文本信息
- 在MIMIC-CXR上优于其他多模态方法和全监督模型
- 适合标签稀缺的医疗场景，如放射科辅助诊断
医学场景中的多模态数据融合与标签稀缺是机器学习的重大挑战。针对这些问题，我们在具有挑战性的MIMIC-CXR数据集上深入评估了新提出的多模态变分混合专家（MMVM）VAE。分析表明，该模型始终优于其他多模态VAE及全监督方法，展现出在真实医疗应用中的强大潜力。
原文摘要 · Abstract (English)
Multimodal data integration and label scarcity pose significant challenges for machine learning in medical settings. To address these issues, we conduct an in-depth evaluation of the newly proposed Multimodal Variational Mixture-of-Experts (MMVM) VAE on the challenging MIMIC-CXR dataset. Our analysis demonstrates that the MMVM VAE consistently outperforms other multimodal VAEs and fully supervised approaches, highlighting its strong potential for real-world medical applications.
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