A cross-modal masked autoencoder creates reusable biosignal fingerprints that match or exceed specialist models on seven cardiovascular tasks using only single-modality input.
arXiv preprint arXiv:2205.14204 (2022)
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EMMS uses evidential fusion based on Dempster-Shafer theory to handle missing modalities in multimodal survival prediction without generative imputation, reporting SOTA results and calibrated uncertainty on four cancer datasets.
Foundation model representations from images and transcriptomics carry complementary signals for cancer classification; multimodal fusion improves results mainly when no modality dominates, and conformal prediction recovers true labels in most failed point predictions on out-of-distribution data.
citing papers explorer
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Biosignal Fingerprinting: A Cross-Modal PPG-ECG Foundation Model
A cross-modal masked autoencoder creates reusable biosignal fingerprints that match or exceed specialist models on seven cardiovascular tasks using only single-modality input.
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Evidential Fusion Network for Multimodal Survival Prediction under Missing Modalities
EMMS uses evidential fusion based on Dempster-Shafer theory to handle missing modalities in multimodal survival prediction without generative imputation, reporting SOTA results and calibrated uncertainty on four cancer datasets.
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Probing, Fusion, and Trustworthiness: A Systematic Evaluation of Foundation Model Representations for Multimodal Cancer Analysis
Foundation model representations from images and transcriptomics carry complementary signals for cancer classification; multimodal fusion improves results mainly when no modality dominates, and conformal prediction recovers true labels in most failed point predictions on out-of-distribution data.