Weakly-Supervised Multimodal Learning on MIMIC-CXR
Published in ML4H Symposium at Neurips 2024, 2024
Recommended citation: A Agostini, D Chopard, Y Meng, N Fortin, B Shahbaba, S Mandt, TM Sutter and JE Vogt (2024) "Weakly-Supervised Multimodal Learning on MIMIC-CXR." ML4H. https://arxiv.org/abs/2411.10356
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.
Recommended citation: A Agostini, D Chopard, Y Meng, N Fortin, B Shahbaba, S Mandt, TM Sutter and JE Vogt. (2024) “Weakly-Supervised Multimodal Learning on MIMIC-CXR.” Ml4H.