The paper introduces the Evidential Missing Modality Survival Fusion (EMMS) model, which predicts survival outcomes using multimodal data even when some modalities are missing. EMMS applies Dempster‑Shafer theory and Gaussian Random Fuzzy Numbers to fuse information, accounting for both aleatoric and epistemic uncertainty and the reliability of each modality. Experiments on four cancer datasets show that EMMS achieves state‑of‑the‑art performance while providing calibrated, interpretable uncertainty estimates without extra computational cost.
arXiv:2607. 20742v1 Announce Type: new Abstract: Multimodal learning is a robust approach to improve predictive performance in applications such as medical prognosis.
arXiv:2607. 27289v1 Announce Type: new Abstract: The promise of multimodal fusion lies in combining complementary sources of evidence, yet more evidence does not always yield a better prediction.
The paper presents a newly curated, multi-center, multi-modal, and longitudinal lung cancer dataset comprising 1,365 patients with whole-slide images, CT scans, PET scans, structured clinical data, transcriptomics, and follow-up information. The dataset features substantial, non-uniform missingness across modalities, making it ideal for evaluating robust multi-modal fusion strategies. Benchmarks on 12‑month overall survival, disease‑specific survival, and longitudinal hazard prediction demonstrate that integrating complementary modalities consistently outperforms uni-modal approaches, even under severe missing data.
arXiv:2606. 12362v1 Announce Type: cross Abstract: We study multimodal learning under missing modalities, with particular motivation from bioscience applications in which heterogeneous modalities are often only partially available when decisions need to be made.
arXiv:2510. 00053v2 Announce Type: replace-cross Abstract: Pathology whole-slide images (WSIs) are widely used for cancer survival analysis because of their comprehensive histopathological information at both cellular and tissue levels, enabling quantitative, large-scale, and prognostically rich tumor feature analysis.
arXiv:2603.08459v2 Announce Type: replace Abstract: Safe predictions are a crucial requirement for integrating predictive models into clinical decision support systems. One approach to improving trus...
arXiv:2608. 02769v1 Announce Type: cross Abstract: Multimodal supervised learning seeks to leverage multiple heterogeneous data sources to improve predictive performance.
arXiv:2605. 25050v2 Announce Type: replace-cross Abstract: Integrating multimodal datasets in clinical oncology is frequently hindered by high dimensionality and blockwise missingness, where entire data sources are unavailable for specific patient subsets.
NeoTriFuse is a reliability‑aware multimodal fusion framework designed to predict neonatal mortality risk from bedside monitoring data that suffers from extreme class imbalance, heterogeneous risk factors, multi‑scale temporal dynamics, and significant missingness. The method treats missing data as an explicit reliability signal, dynamically adjusting modality contributions during fusion through gating mechanisms that incorporate static perinatal variables, local‑global temporal encoders, and patient‑level statistical summaries. NeoTriFuse achieves competitive performance (F1 ≈ 0.674, AUROC ≈ 0.945) and ablation studies show that its temporal architecture and patient‑level summary branch are key contributors, with reliability‑aware gating further improving threshold‑dependent metrics under heterogeneous observation completeness.
arXiv:2607. 09982v1 Announce Type: new Abstract: Electronic health record (EHR) data are inherently multimodal, and leveraging multiple modalities can improve predictive performance.
arXiv:2608. 07183v1 Announce Type: new Abstract: Multimodal fusion architectures typically assume all modalities are available at inference, yet sensor failures, acquisition variability, and cost constraints routinely produce incomplete observations.
arXiv:2606. 17115v1 Announce Type: cross Abstract: Foundation models (FMs) have emerged as powerful representation extractors for medical data, yet their generalizability to datasets under distribution shift remains underexplored.