Weakly-Supervised Multimodal Learning on MIMIC-CXR

Autor:	Agostini, Andrea, Chopard, Daphné, Meng, Yang, Fortin, Norbert, Shahbaba, Babak, Mandt, Stephan, Sutter, Thomas M., Vogt, Julia E.
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Findings paper presented at Machine Learning for Health (ML4H) symposium 2024, December 15-16, 2024, Vancouver, Canada, 13 pages. arXiv admin note: text overlap with arXiv:2403.05300
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2411.10356 Zobrazit plný text záznamu View this record from Arxiv