arXiv subjects

Roland Eils

Publications and source records attributed to Roland Eils.

12 recordsLinked to original sources

arXiv2026

Beyond Objective Expressivity: Geometry Preservation in Multimodal Contrastive Learning

Contrastive learning is increasingly moving toward settings with three or more modalities instead of image-text pairs. Yet, extending models from pairwise to higher-order multimodal alignment can introduce optimization and representation challenges. We identify encoder Jacobian conditioning as a key factor in trimodal contrastive learning: poorly conditioned encoders exhibit collapsing or amplified singular-value spectra, leading to exploding Jacobian condition numbers and degraded multimodal alignment. We introduce geometry-preserving encoders (GPEs) by directly conditioning the Jacobian through regularization and demonstrating that simple modifications like LeakyReLU activations and residual paths recover these geometric benefits. Across a synthetic benchmark and four real-world datasets including missing modalities, improving Jacobian conditioning boosts retrieval and linear probe performance across multiple contrastive objectives, whereas expressive objectives yield little benefit in linear probes. More broadly, our results show that multimodal contrastive learning depends not only on objective expressivity, but also on the geometric and optimization properties of the underlying encoders.

cs.LG↗

arXiv2026

Modeling Local, Global, and Cross-Modal Context in Multimodal 3D MRI

Brain MRI poses a fundamental challenge for machine learning: models must learn from high-dimensional 3D data spanning multiple co-registered modalities, despite the limited sample sizes typical of neuroimaging studies relative to the diversity in anatomy, pathology, and acquisition conditions. While multimodal imaging provides complementary information critical for clinical interpretation, effectively integrating these signals remains difficult. We propose Multimodal Intra- and Cross-Context Vision Transformer (MICViT), a 3D vision transformer that explicitly models both modality-specific representations and cross-modal interactions across local and global contexts. Concretely, MICViT combines four attention mechanisms: modality-specific local and global attention for intra-modal feature learning, and cross-modal local and global attention to capture interactions between modalities. We evaluate MICViT on brain age prediction across three heterogeneous datasets (UK Biobank, n=41,404; SOOP, n=1,062; Cam-CAN, n=613) using multiple MRI modalities (e.g. T1, FLAIR, DWI, SWI). MICViT consistently outperforms state-of-the-art CNN and transformer baselines in 3D settings. Notably, it benefits more strongly from multimodal inputs, yielding larger performance gains as additional modalities are incorporated. These results demonstrate that explicitly modeling intra- and cross-modal interactions is key to unlocking the full potential of multimodal brain MRI, highlighting a promising direction for representation learning in neuroimaging.

cs.CV↗

arXiv2026

Human-in-the-Loop Multi-Agent Ventilator Decision Support with Contextual Bandit Preference Learning

Ventilator decision support requires sequential decisions that track evolving physiology and disease trajectories while respecting safety boundaries and clinician specific tuning styles. Rule based approaches rarely generalize personalization, and end to end reinforcement learning or single large language model systems remain difficult to control and audit. We propose the Ventilator Decision Support System (VDSS), a human in the loop multi agent framework that coordinates modular decision components through contract driven structured interfaces and produces traceable evidence for review. VDSS performs online preference adaptation with a contextual bandit, updating clinician specific preferences from the final accepted decision at each adjustment cycle and using them to guide subsequent recommendations. Structured rejection feedback triggers targeted replanning to reduce unproductive iterations and improve interaction stability. Retrospective ICU trajectory replay with expert review indicates higher recommendation acceptability and fewer interaction rounds to reach an acceptable plan, supporting clinically deployable human AI collaboration.

cs.AI↗

arXiv2026

Bridging openEHR and OMOP: Expanded Mappings and Systematic Analysis of Semantic and Structural Limitations in the OMOP CDM

Background: Interoperability between clinical and research data systems is essential for enabling secondary use of EHR data. The openEHR standard provides structured, model-driven clinical information, while the OMOP Common Data Model (CDM) supports large-scale observational analytics. The Eos engine and OMOP Conversion Language (OMOCL) previously introduced a standards-based transformation approach, but limited value set support, rigid visit generation, and incomplete mapping coverage restricted broader applicability. Methods: A new generation of Eos and OMOCL was implemented to improve semantic completeness and address earlier limitations. New functionality enables mapping of internal openEHR value sets via conceptMaps, supports visit occurrence generation using Archetype Query Language (AQL), and expands the international archetype mapping library. The framework was evaluated by assessing mapping coverage, terminology completeness, and domain distribution. Structural constraints of OMOP were examined using representative archetype mappings. Results: 196 openEHR archetypes were mapped, covering all stable archetypes in the international Clinical Knowledge Manager with OMOP-equivalent tables. 8.65% of primary concept identifiers could not be linked to OMOP standard terminologies. Most mappings targeted the Measurement (50.5%) and Observation (41.0%) domains. Structural analysis showed that coherent clinical concepts often required fragmentation across multiple loosely connected OMOP tables; the Problem/Diagnosis archetype alone required more than 20 linked records. Conclusions: The new framework strengthens openEHR-OMOP interoperability and reduces information loss. However, structural and semantic limitations within OMOP introduce fragmentation that may affect downstream analytics, suggesting a need for greater convergence between both ecosystems.

cs.DL↗

arXiv2026

Hidden in the Multiplicative Interaction: Uncovering Fragility in Multimodal Contrastive Learning

Contrastive learning has become a standard approach for unsupervised learning from paired data, as demonstrated by CLIP for image-text matching. However, many domains involve more than two modalities and require objectives that capture higher-order dependencies beyond pairwise alignment. Symile extends CLIP to this setting by replacing the dot product with the multilinear inner product (MIP) over modality embeddings. In this work, we show that there is a fragility which ishidden in the multiplicative interaction: a single weakly informative, misaligned, or missing modality can propagate through the objective and distort cross-modal retrieval scores. We propose Gated Symile, a contrastive gating mechanism that adapts modality contributions on an attention-based, per-candidate basis. The gate suppresses unreliable inputs by interpolating embeddings toward learnable neutral directions with an explicit NULL option when reliable cross-modal alignment is unlikely. Across a controlled synthetic benchmark that uncovers this fragility and three real-world trimodal datasets, Gated Symile achieves higher top-1 retrieval accuracy than well-tuned state-of-the-art (sota) baselines. More broadly, our results highlight gating as a step toward robust multimodal contrastive learning beyond two modalities in the presence of noise, misalignment, or missing inputs.

cs.LG↗

arXiv2025

Fusion or Confusion? Multimodal Complexity Is Not All You Need

Multimodal learning has become a prominent research area, with the potential of substantial performance gains by combining information across modalities. At the same time, model development has trended toward increasingly complex deep learning architectures, motivated by the assumption that multimodal-specific methods improve performance. We challenge this assumption through a large-scale empirical study by reimplementing 19 high-impact multimodal methods across nine diverse datasets with up to 23 modalities. Under standardized experimental conditions, including hyperparameter tuning, weight initialization, cross-validation, and statistical testing, increased multimodal complexity often yields confusion rather than effective fusion of data modalities. Accordingly, complex multimodal architectures do not reliably outperform unimodal baselines and a Simple Baseline for Multimodal Learning (SimBaMM). Through a focused case study, we further demonstrate concrete methodological shortcomings even in top-tier multimodal learning publications, underscoring the need for standardized evaluation practices. In summary, we argue for a shift in focus for multimodal learning: away from the pursuit of architectural novelty and toward methodological rigor.

cs.LG↗

arXiv2025

FHIRconnect: Towards a seamless integration of openEHR and FHIR

Healthcare interoperability between openEHR and HL7 FHIR remains challenging due to fundamental differences in their data modeling approaches and the absence of standardized transformation mechanisms. This paper presents FHIRconnect, a novel domain-specific language and open-source transformation engine that enables standardized, bidirectional data exchange between openEHR and FHIR. Our approach addresses critical interoperability gaps through a triple-layered architecture that achieves 65% mapping reuse across projects by leveraging international archetype-based foundations while supporting local customizations. Using this framework, FHIRconnect successfully mapped 24 international archetypes to 15 FHIR profiles across seven clinical domains. Key contributions include the first comprehensive DSL for openEHR-FHIR transformation with a formal specification, an open-source execution engine (openFHIR), and an accessible mapping library covering high-impact clinical archetypes. Together, these components establish the technical basis for community-driven mapping standardization, reducing reliance on custom ETL solutions and advancing syntactic and semantic interoperability in healthcare IT systems built on open standards.

cs.SE↗

arXiv2025

Cohort-Based Active Modality Acquisition

Real-world multimodal machine learning often faces missing, costly-to-acquire modalities, raising the problem of which samples to prioritize for additional acquisition under a budget. Prior work mainly studies per-sample or training-time acquisition while test-time, cohort-level acquisition is less explored. We propose Cohort-based Active Modality Acquisition (CAMA), a novel test-time cohort-level modality acquisition setting, and introduce imputation-based acquisition strategies that estimate the expected utility of acquiring a missing modality, along with upper-bound heuristics for benchmarking. Experiments on datasets with up to 15 modalities demonstrate that our proposed imputation-based strategies can more effectively guide the acquisition of an additional modality for selected samples compared with methods relying solely on pre-acquisition information, entropy-based guidance, or random selection. We showcase the real-world relevance and scalability of our method by demonstrating its ability to guide the acquisition of proteomics data for disease prediction in a large prospective cohort, the UK Biobank (UKB). Our work provides an effective approach for optimizing modality acquisition at the cohort level, enabling more effective use of resources in constrained settings.

cs.LG↗

arXiv2025

JanusDNA: A Powerful Bi-directional Hybrid DNA Foundation Model

Large language models (LLMs) have revolutionized natural language processing and are increasingly applied to other sequential data types, including genetic sequences. However, adapting LLMs to genomics presents significant challenges. Capturing complex genomic interactions requires modeling long-range dependencies within DNA sequences, where interactions often span over 10,000 base pairs, even within a single gene, posing substantial computational burdens under conventional model architectures and training paradigms. Moreover, standard LLM training approaches are suboptimal for DNA: autoregressive training, while efficient, supports only unidirectional understanding. However, DNA is inherently bidirectional, e.g., bidirectional promoters regulate transcription in both directions and account for nearly 11% of human gene expression. Masked language models (MLMs) allow bidirectional understanding but are inefficient, as only masked tokens contribute to the loss per step. To address these limitations, we introduce JanusDNA, the first bidirectional DNA foundation model built upon a novel pretraining paradigm that combines the optimization efficiency of autoregressive modeling with the bidirectional comprehension of masked modeling. JanusDNA adopts a hybrid Mamba, Attention and Mixture of Experts (MoE) architecture, combining long-range modeling of Attention with efficient sequential learning of Mamba. MoE layers further scale model capacity via sparse activation while keeping computational cost low. Notably, JanusDNA processes up to 1 million base pairs at single nucleotide resolution on a single 80GB GPU. Extensive experiments and ablations show JanusDNA achieves new SOTA results on three genomic representation benchmarks, outperforming models with 250x more activated parameters. Code: https://github.com/Qihao-Duan/JanusDNA

cs.LG↗

arXiv2025

Large Language Models are Powerful Electronic Health Record Encoders

Electronic Health Records (EHRs) offer considerable potential for clinical prediction, but their complexity and heterogeneity challenge traditional machine learning. Domain-specific EHR foundation models trained on unlabeled EHR data have shown improved predictive accuracy and generalization. However, their development is constrained by limited data access and site-specific vocabularies. We convert EHR data into plain text by replacing medical codes with natural-language descriptions, enabling general-purpose Large Language Models (LLMs) to produce high-dimensional embeddings for downstream prediction tasks without access to private medical training data. LLM-based embeddings perform on par with a specialized EHR foundation model, CLMBR-T-Base, across 15 clinical tasks from the EHRSHOT benchmark. In an external validation using the UK Biobank, an LLM-based model shows statistically significant improvements for some tasks, which we attribute to higher vocabulary coverage and slightly better generalization. Overall, we reveal a trade-off between the computational efficiency of specialized EHR models and the portability and data independence of LLM-based embeddings.

cs.LG↗

arXiv2023

Diffsurv: Differentiable sorting for censored time-to-event data

Survival analysis is a crucial semi-supervised task in machine learning with numerous real-world applications, particularly in healthcare. Currently, the most common approach to survival analysis is based on Cox's partial likelihood, which can be interpreted as a ranking model optimized on a lower bound of the concordance index. This relation between ranking models and Cox's partial likelihood considers only pairwise comparisons. Recent work has developed differentiable sorting methods which relax this pairwise independence assumption, enabling the ranking of sets of samples. However, current differentiable sorting methods cannot account for censoring, a key factor in many real-world datasets. To address this limitation, we propose a novel method called Diffsurv. We extend differentiable sorting methods to handle censored tasks by predicting matrices of possible permutations that take into account the label uncertainty introduced by censored samples. We contrast this approach with methods derived from partial likelihood and ranking losses. Our experiments show that Diffsurv outperforms established baselines in various simulated and real-world risk prediction scenarios. Additionally, we demonstrate the benefits of the algorithmic supervision enabled by Diffsurv by presenting a novel method for top-k risk prediction that outperforms current methods.

cs.LG↗

arXiv2018

The Human Cell Atlas White Paper

The Human Cell Atlas (HCA) will be made up of comprehensive reference maps of all human cells - the fundamental units of life - as a basis for understanding fundamental human biological processes and diagnosing, monitoring, and treating disease. It will help scientists understand how genetic variants impact disease risk, define drug toxicities, discover better therapies, and advance regenerative medicine. A resource of such ambition and scale should be built in stages, increasing in size, breadth, and resolution as technologies develop and understanding deepens. We will therefore pursue Phase 1 as a suite of flagship projects in key tissues, systems, and organs. We will bring together experts in biology, medicine, genomics, technology development and computation (including data analysis, software engineering, and visualization). We will also need standardized experimental and computational methods that will allow us to compare diverse cell and tissue types - and samples across human communities - in consistent ways, ensuring that the resulting resource is truly global. This document, the first version of the HCA White Paper, was written by experts in the field with feedback and suggestions from the HCA community, gathered during recent international meetings. The White Paper, released at the close of this yearlong planning process, will be a living document that evolves as the HCA community provides additional feedback, as technological and computational advances are made, and as lessons are learned during the construction of the atlas.

q-bio.TO↗