MuteBench evaluates multimodal fusion robustness to modality missing and within-modality missing on 125000 samples from 9 clinical datasets, finding architecture family predicts tolerance better than parameter count.
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SGC-RML creates an 8D symptom atlas from multimodal PD data and integrates conformal calibration to deliver reliable, rejectable longitudinal assessments.
Multimodal contrastive learning using multilinear products is fragile to single bad modalities, and a gated version improves top-1 retrieval accuracy on synthetic and real trimodal data.
Multimodal LLMs achieve far lower diagnostic accuracy on real hospital dermatology cases than on public benchmarks, with added clinical context helping but not enough for reliable deployment.
MediSyn is a generalist latent diffusion model that synthesizes text-guided medical images across multiple specialties and modalities from public data and improves downstream classifiers in low-data settings.
Scoping review of 30 papers finds that vertical methods for linear and logistic regression on partitioned health data rarely achieve equivalence to pooled analyses while also being communication-efficient and verifiably private.
citing papers explorer
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MuteBench: Modality Unavailability Tolerance Evaluation for Incomplete Multimodal Fusion
MuteBench evaluates multimodal fusion robustness to modality missing and within-modality missing on 125000 samples from 9 clinical datasets, finding architecture family predicts tolerance better than parameter count.
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SGC-RML: A reliable and interpretable longitudinal assessment for PD in real-world DNS
SGC-RML creates an 8D symptom atlas from multimodal PD data and integrates conformal calibration to deliver reliable, rejectable longitudinal assessments.
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Hidden in the Multiplicative Interaction: Uncovering Fragility in Multimodal Contrastive Learning
Multimodal contrastive learning using multilinear products is fragile to single bad modalities, and a gated version improves top-1 retrieval accuracy on synthetic and real trimodal data.
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Are Multimodal LLMs Ready for Clinical Dermatology? A Real-World Evaluation in Dermatology
Multimodal LLMs achieve far lower diagnostic accuracy on real hospital dermatology cases than on public benchmarks, with added clinical context helping but not enough for reliable deployment.
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A Generalist Model for Diverse Text-Guided Medical Image Synthesis
MediSyn is a generalist latent diffusion model that synthesizes text-guided medical images across multiple specialties and modalities from public data and improves downstream classifiers in low-data settings.
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Multi-Site Health Research Integrating Complementary Data Sources: A Scoping Review of Statistical Inference Methods for Vertically Partitioned Data
Scoping review of 30 papers finds that vertical methods for linear and logistic regression on partitioned health data rarely achieve equivalence to pooled analyses while also being communication-efficient and verifiably private.