PiCME shows contrastive learning peaks at three modalities in MIMIC, and a Modality-Gated LSTM with contrastively learned weights improves five-modality mortality prediction over supervised baselines.
Weakly-Supervised Multimodal Learning on MIMIC-CXR
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abstract
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.
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PiCME: Pipeline for Contrastive Modality Evaluation and Encoding in the MIMIC Dataset
PiCME shows contrastive learning peaks at three modalities in MIMIC, and a Modality-Gated LSTM with contrastively learned weights improves five-modality mortality prediction over supervised baselines.