# Weakly-Supervised Multimodal Learning on MIMIC-CXR

arXiv: https://arxiv.org/abs/2411.10356 · HTML: https://mlsanity.com/paper/2411.10356/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2024.

## Built on by (most cited first, 3 of 3)

- [PiCME: Pipeline for Contrastive Modality Evaluation and Encoding in the MIMIC Dataset](https://mlsanity.com/paper/2507.03165/) (2025)
- [Leveraging the Structure of Medical Data for Improved Representation Learning](https://mlsanity.com/paper/2507.02987/) (2025)
- [Structure is Supervision: Multiview Masked Autoencoders for Radiology](https://mlsanity.com/paper/2511.22294/) (2025)

## Abstract (arXiv)

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.