arXiv AI

Probing, Fusion, and Trustworthiness: A Systematic Evaluation of Foundation Model Representations for Multimodal Cancer Analysis

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.

arXiv Machine Learning

Jul 24

Adaptive Confidence-weighted Expansion for Trustworthy Multi-Omics Multimodal Fusion

arXiv:2607. 20742v1 Announce Type: new Abstract: Multimodal learning is a robust approach to improve predictive performance in applications such as medical prognosis.

By Mohammad Raahemi, Ali Sekhavati, Alireza Maleki, Hamid Nasiri

multimodal benchmarks safety data-engineering

arXiv AI

Jun 16

Fusion is not one-size-fits-all: Cross-Modal Representation Alignment for Time-to-Event Modeling

arXiv:2606. 15038v1 Announce Type: new Abstract: Accurate time-to-event (TTE) prediction from multimodal clinical data remains challenging due to modality imbalance and distribution shift.

By Zhemin Zhang, Weijie Chen, David Le, Amara Tariq, Alex Wallace, Matthew Stib, Juan Maria Farina, Chadi Ayoub, Reza Arsanjani, Imon Banerjee

diffusion multimodal safety

arXiv Machine Learning

Jul 31

TIER-MoE: Trust-Informed Expert Routing via Conditional Modality Risk for Multimodal Fusion in Biomedical Classification

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.

By Yu Chang, Anzhe Cheng, Chenwei Wu, Zhuoran Wang, Jiahao Chen, Tamoghna Chattopadhyay, Sophia I. Thomopoulos, Paul M. Thompson, Liyue Shen, Paul Bogdan

multimodal benchmarks

arXiv Machine Learning

Jul 9

MedPMC: A Systematic Framework for Scaling High-Fidelity Medical Multimodal Data for Foundation Models

arXiv:2607. 07673v1 Announce Type: cross Abstract: Medicine is inherently multimodal, requiring clinicians to synthesize information across diverse data streams.

By Hyunjae Kim, Dain Kim, Pan Xiao, Serina S. Applebaum, Younjoon Chung, Xuguang Ai, Yu Yin, Roy Jiang, Yuexi Du, Yawen Wei, Yiming Kong, Tuo Guo, Zhiyuan Cao, Mengmeng Du, Yuelei Fu, Yan Hu, Rui Shi, Gui Yang, Kevin W. Jin, Yuntian Liu, Yuxuan Tian, Jonathan Marquez, Zhen Chen, Sheng Zhang, Hoifung Poon, Hua Xu, Jaewoo Kang, Qingyu Chen

llms multimodal benchmarks safety

arXiv Machine Learning

Jul 14

Multimodal Routing for Interpretable, Robust, and Auditable Clinical Prediction

arXiv:2607. 09982v1 Announce Type: new Abstract: Electronic health record (EHR) data are inherently multimodal, and leveraging multiple modalities can improve predictive performance.

By Nikkie Hooman, Zhongjie Wu, Eric C. Larson, Mehak Gupta

multimodal safety

arXiv Machine Learning

Aug 5

DAIF: A Data-Driven Intermediate Fusion Framework for Multimodal Supervised Learning via Approximate Message Passing

arXiv:2608. 02769v1 Announce Type: cross Abstract: Multimodal supervised learning seeks to leverage multiple heterogeneous data sources to improve predictive performance.

By Sagnik Nandy, Samriddha Lahiry, Pragya Sur, Subhabrata Sen

rag multimodal benchmarks

arXiv Machine Learning

Jul 24

Multimodality Stacking with Blockwise missing values and application to the PIONeeR biomarkers study for prediction of resistance to immunotherapy

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.

By Mohamed Boussena, Florence Monville, Jacques Fieschi-Meric, Frederic Vely, Pierre Milpied, Julien Mazieres, Maurice Perol, Eric Vivier, Laurent Greillier, Fabrice Barlesi, Sebastien Benzekry

multimodal

Hugging Face Trending Papers

Jul 21

Advancing Multimodal Fusion on Heterogeneous Medical Data with Hybrid Geometry Attention

Multimodal fusion learning (MFL) has shown great potential in the medical domain, where we are faced with disparate data modalities such as imaging, clinical records, and omics. However, existing MFL strategies face several major challenges.

multimodal

arXiv Machine Learning

Jul 7

Beyond Modality Fusion: Deep Ensembles for Multimodal Classification

arXiv:2607. 05019v1 Announce Type: new Abstract: In multimodal classification, late-fusion approaches classify concatenated modality-specific features extracted by unimodal neural networks.

By Ilya Burenko, Dmitry Vetrov

multimodal benchmarks

arXiv AI

Jul 21

AdaSurvMamba: Dynamic Fusion and Semantic Scanning for Multimodal Survival Analysis

arXiv:2607. 16260v1 Announce Type: cross Abstract: Multimodal survival analysis utilizing whole slide images (WSIs) and genomic profiles is fundamental for cancer prognosis.

By Jialong Zhong, Tingwei Liu, Baokun Yue, Jingjing Li, Yongri Piao, Miao Zhang, Leiye Liu, Jiahong Jiang, Wei Ji, Huchuan Lu

multimodal

arXiv AI

Aug 11

DoGMA: A Central-Dogma-Guided Foundation Model for Multi-Omics Alignment and Multi-Task Learning in Oncology

arXiv:2608. 08148v1 Announce Type: cross Abstract: Attention mechanisms have been widely utilized in modern deep learning, and many existing multi-omics models inherit their conventional use to allow unrestricted bidirectional interactions.

By Junfei Ling (Institute of Medical Robotics, Shanghai Jiao Tong University), Bangzheng Pu (Institute of Medical Robotics, Shanghai Jiao Tong University), Bingsen Xue (Institute of Medical Robotics, Shanghai Jiao Tong University), Tianle Li (Institute of Data Science, The University of Hong Kong), Ruying Hu (Oriental Pan-Vascular Devices Innovation College, University of Shanghai for Science and Technology), Cheng Jin (Institute of Medical Robotics, Shanghai Jiao Tong University)

llms safety

arXiv AI

Jun 30

Data-Efficient Multimodal Alignment for Histopathology-based Molecular Prediction

arXiv:2606. 29949v1 Announce Type: cross Abstract: H&E-stained whole-slide images offer cohort-scale availability and rich spatial context but lack molecular specificity, whereas bulk RNA-seq provides transcriptome-wide resolution at high cost with limited archival availability.

By Dominik Winter, Dominik Vonficht, Lo\"ic Le Bescond, Christian Gebbe, Marco Rosati, Richard J. Chen, Markus Schick, Ross Stewart, Nicolas Brieu

llms fine-tuning multimodal safety

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