Weakly-Supervised Multimodal Learning on MIMIC-CXR
Published in Machine Learning for Health (ML4H) symposium 2024, 2024
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.
Keywords: Multimodal Learning; Representation Learning, Multimodal Learning, Medical Imaging Analysis, Chest X-rays
Recommended citation: Agostini A., Chopard D., Meng Y., Fortin N., Shahbaba B., Mandt S., Sutter T. M., Vogt J. E. (2024). "Weakly-Supervised Multimodal Learning on MIMIC-CXR" arXiv Preprint arXiv: 2411.10356, 2024. https://arxiv.org/pdf/2411.10356