Summary of Weakly-supervised Multimodal Learning on Mimic-cxr, by Andrea Agostini et al.
Weakly-Supervised Multimodal Learning on MIMIC-CXR
by Andrea Agostini, Daphné Chopard, Yang Meng, Norbert Fortin, Babak Shahbaba, Stephan Mandt, Thomas M. Sutter, Julia E. Vogt
First submitted to arxiv on: 15 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Multimodal Variational Mixture-of-Experts (MMVM) VAE model is evaluated on the challenging MIMIC-CXR dataset to address issues of multimodal data integration and label scarcity in medical settings. The MMVM VAE outperforms other multimodal VAEs and fully supervised approaches, showcasing its potential for real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers develop a new machine learning model called the Multimodal Variational Mixture-of-Experts (MMVM) VAE to help with medical diagnosis. They test it on a big dataset of chest X-ray images and show that it works better than other similar models or those trained only with labeled data. |
Keywords
* Artificial intelligence * Machine learning * Mixture of experts * Supervised
