arXiv:2411.10356 공식 코드 · Papers with Code

Weakly-Supervised Multimodal Learning on MIMIC-CXR

Andrea Agostini, Daphné Chopard, Yang Meng, Norbert Fortin, Babak Shahbaba, Stephan Mandt, Thomas M. Sutter, Julia E. Vogt

제출 2024-11-15

원문 초록

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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arXiv:2507.06085공통 태그 Mixture-of-Experts공식 코드 · Papers with Code

A Survey on Prompt Tuning

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