Speech activity recognition in MEG-based non-invasive BCI systems provides a reliable speech gate that can trigger downstream decoders only when speech-related neural activity is present. Such a gate can help users interact with assistive devices in continuous settings. While MEG provides excellent temporal resolution, many MEG speech activity classifiers do not fully exploit available spatial information. We describe a hybrid MEG speech/non-speech classifier that combines a geometry-aware covariance branch with temporal neural streams. We compute shrinkage covariance matrices (SPD) and map them to a Riemannian tangent space around a reference mean; a logistic regression classifier operates on these features. A limited sensor array defines the region-of-interest component, while the final system adds a residual temporal fusion layer over aligned probability streams. The final decision rule, including fusion weights, thresholds, and sequence-level post-processing, is selected on validation only and then applied unchanged to frozen test. On a large within-subject MEG corpus, the final system achieved validation macro-F1 of 0.8972 and frozen-test macro-F1 of 0.8914. This provides a compute-efficient research prototype for MEG-based speech gating.
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Large vision-language models (VLMs) demonstrate strong performance in medical image understanding, but frequently generate clinically plausible yet incorrect statements, raising significant safety concerns. Existing medical hallucination benchmarks primarily focus on 2D imaging with one-shot diagnostic questions, offering limited insight into whether predictions are grounded in correct localization and abnormality identification, allowing critical reasoning errors to remain hidden behind seemingly correct diagnoses. We introduce Med-StepBench, the first large-scale benchmark for step-wise hallucination detection in 3D oncological PET/CT, comprising over 12,000 images and more than 1,000,000 image–statement pairs across volumetric and multi-view 2D data, which decomposes clinical reasoning into four expert-designed diagnostic stages. Using clinician-verified annotations, we perform the first step-level evaluation of general-purpose and medical VLMs, revealing systematic failure modes obscured by aggregate accuracy metrics. Furthermore, we show that current VLMs are highly susceptible to adversarial yet clinically plausible intermediate explanations, which significantly amplify hallucinations despite contradictory visual evidence. Together, our findings highlight fundamental limitations in grounding multi-step clinical reasoning and establish Med-StepBench as a rigorous benchmark for developing safer and more reliable medical VLMs.
Non-pharmaceutical interventions (NPIs), such as diagnostic testing and quarantine, are crucial for controlling infectious disease outbreaks but are often constrained by limited resources, particularly in the early outbreak stages. In real-world public health settings, resources must be allocated across multiple outbreak clusters that emerge asynchronously, vary in size and risk, and compete for a shared resource budget. We define a cluster as a group of close contacts generated by a single infected index case. Thus, decisions must be made under uncertainty and heterogeneous demands while respecting operational constraints. We formulate this problem as a constrained restless multi-armed bandit and propose a hierarchical reinforcement learning framework. A global controller learns a continuous action cost multiplier that adjusts global resource demand, while a generalized local policy estimates the marginal value of allocating resources to individuals within each cluster. We evaluate the proposed framework in a realistic agent-based simulator of SARS-CoV-2 with dynamically arriving clusters. Across a wide range of system scales and testing budgets, our method consistently outperforms RMAB-inspired and heuristic baselines, improving outbreak control effectiveness by 20--30%. Experiments on up to 40 concurrently active clusters further demonstrate that the hierarchical framework is highly scalable and enables faster decision-making than the RMAB-inspired method.
Extracting multi-step explanations from knowledge graphs poses a combinatorial challenge requiring both heuristic guidance (as candidates proliferate with depth) and credit assignment (as path quality emerges over extended sequences). Frontier LLMs, strong on knowledge/reasoning benchmarks, offer a compelling source of such heuristics, yet their knowledge comes sans guarantees and compositional performance degrades as chains lengthen. We thus present TESSERA, a 3-part neuro-symbolic framework that uses LLMs in a circumscribed role: for local discriminative judgement rather than autonomous multi-step generation; the knowledge graph then defines the hypothesis space enforcing hard structural constraints, and MCTS coordinates the long-horizon search with principled credit assignment via backpropagation. LLMs perform dual roles as a prior policy biasing exploration and a comparative state evaluator supplying reward signals. Evaluation on drug mechanism elucidation across two complementary knowledge graphs demonstrates fidelity to curated biology while surfacing coherent alternative mechanisms, with ablations confirming discriminative contribution from both LLM components. Beyond its current application, our framework offers a general paradigm for compositional reasoning over structured knowledge.
3D dental segmentation is a key task in digital dentistry. In real intraoral scans data (IOS), occlusion, scanning noise, and reconstruction artifacts often break down the geometric separation structure between teeth, resulting in adjacent teeth being incorrectly merged or a single tooth being over-segmented. Since existing point cloud or mesh-based methods usually rely on local neighborhood consistency, when there are spurious geometric connections, features will diffuse across instances along geometric shortcuts, resulting in instance-level error propagation. To address this issue, we propose VGDM (Visual-Guided Diffusion Modulation), which serves as a bridge between extrinsic visual localization cues and intrinsic surface features under detection settings, enabling the extrinsic cues to regulate the propagation of intrinsic surface features. Instead of global propagation on the entire intraoral scan mesh, VGDM uses the single-view 2D detection results to roughly localize the tooth region and construct local 3D surface patches based on it. Within the patch, we soft-constrain feature propagation by visual cues to suppress the cross-instance propagation generated along spurious geometric connections, and introduce a dual-stream diffusion structure to improve the overall robustness. Experimental results on the largest public intraoral dataset(Teeth3DS) show that VGDM can significantly improve the segmentation rate of tooth instances and effectively reduce the merging and over-segmentation of adjacent teeth.
MedFiTRG: Jointly Learning Dynamic Temporal and Cross-Patient Graphs for Clinical Outcome Prediction
Integrating heterogeneous clinical modalities, structured electronic health records (EHRs), clinical text, and medical imaging is crucial for reliable clinical prediction, yet real-world data are often sparse and imbalanced. Furthermore, prior approaches treat temporal dynamics and inter-patient relationships in isolation, overlooking the dynamic interaction of patient trajectories across populations. We introduce a modality-enhanced dynamic temporal relational graph (MedFiTRG), a unified framework that jointly models sparsity, temporal dynamics, and cross-patient relational dependencies. MedFiTRG leverages modulated graph neural networks (MGNN) to learn modality-aware embeddings, enabling meaningful representation of sparse modalities through adaptive feature modulation. These embeddings are integrated into a temporal relational graph (TRG), where directed intra-patient edges capture longitudinal progression and dynamic inter-patient edges model population-level similarities for synchronized temporal-relational reasoning. Extensive experiments on large-scale real-world datasets across four clinical tasks demonstrate that MedFiTRG achieves superior or comparable performance against state-of-the-art baselines, improving Macro-F1 from 0.155 to 0.310 for length of stay (LOS) classification and achieving an AUROC of 0.939 for mortality prediction (↑6.45%). The code is available at https://anonymous.4open.science/r/MedFiTRG-2714
Accurate volumetric medical image segmentation is critical for clinical diagnosis, yet adapting two-dimensional vision foundation models (VFMs) to three-dimensional medical imaging remains challenging. While parameter-efficient fine-tuning (PEFT) methods such as LoRA and adapter-based schemes provide efficient alternatives to full fine-tuning, their geometry-agnostic parameter-space adaptations are insufficient to reconcile discrepancies induced by anisotropic voxel spacing and inter-slice discontinuities. We propose GeoSFLoRA, a geometry-constrained spectral-flow low-rank adaptation framework for efficient 2D-to-3D transfer learning. GeoSFLoRA adapts frozen 2D pretrained vision backbones by operating within a fixed low-rank spectral subspace induced by pretrained linear projections. For each adapted layer, a truncated singular value decomposition is computed once and kept frozen, preserving the original pretrained bases. A lightweight Geometry-Conditioned Encoder (GCE) extracts local volumetric descriptors, which are mapped to token-conditional residuals in the singular-value space, enabling bounded and geometry-aware spectral modulation. GeoSFLoRA consistently improves Dice and HD95 on BraTS20, MSD-Prostate, and MSD-Lung, approaching full fine-tuning performance and demonstrating an effective paradigm for 2D-to-3D medical image segmentation. The code is publicly available at https://github.com/chenbn266/GeoSFLoRA.
As a vital task in healthcare, combinatorial medication recommendation aims to generate drug combinations tailored to patient health status. Precisely capturing the rich semantic information within clinical narratives is crucial for achieving this goal. However, existing approaches primarily rely on isolated identifiers (i.e. patient IDs, drug codes), failing to leverage the inherent semantic associations between patient conditions and medication descriptions. To fill this gap, we propose the Dual-Channel Semantics-Enhanced Network (DCSENet), a novel dual-channel framework that explicitly incorporates context-rich clinical narratives knowledge. DCSENet fine-tunes domain-adapted pre-trained language models (LMs) to capture semantic correlations between patient status and medication narratives. A transformer-based dual-channel decoder decodes the semantic information at the disease-level and the patient-level respectively. The disease-level channel focuses on the natural text semantic associations between diseases and drugs, while the patient-channel provides personalized features. To mitigate the prohibitive computational overhead of the LMs in clinical deployment, we introduce an attention-map-based knowledge distillation mechanism that efficiently transfers semantic knowledge from the LMs into an identifier-based (ID-based) target model. Extensive experiments on MIMIC-Ⅲ and MIMIC-Ⅳ datasets demonstrate that DCSENet outperforms existing state-of-the-art methods in recommendation accuracy while maintaining a low computational cost.
Hypergraphs effectively model complex interactions in structured Electronic Health Records (EHR). Consequently, Hypergraph Neural Networks (HGNNs) are commonly applied to EHR-based diagnosis prediction. However, existing HGNNs struggle to capture patient-visit long-range dependencies when processing EHR-derived hypergraphs. To tackle this issue, we propose a Patient-Visit-Spanned HyperGraph Learning (PVHGL) framework specifically designed for diagnosis prediction. Concretely, PVHGL initially constructs a unified patient-visit hypergraph that integrates visit records from all patients, enabling the capture of shared healthcare patterns across the patient population. Subsequently, it incorporates a Transformer architecture enhanced by two structure-encoding matrices to facilitate one-step message propagation, which preserves both local and global hypergraph structural properties effectively. Additionally, the framework integrates a medical code co-occurrence matrix to explicitly guide the learning process by highlighting critical medical code interactions. Comprehensive experiments on three real-world datasets demonstrate that the proposed PVHGL significantly outperforms state-of-the-art baselines in diagnosis prediction.
Fluorescence molecular tomography (FMT) serves as a pivotal modality for preclinical tumor screening. While single-view FMT offers distinct advantages in data acquisition efficiency and cost-effectiveness, the scarcity of projection views severely exacerbates photon scattering-induced depth ambiguity, rendering 3D volumetric recovery a highly ill-posed inverse problem. To address these challenges, we propose a physics-inspired spectral topology aware reconstruction network (STAR-Net). Specifically, STAR-Net establishes a synergistic framework: initially, a frequency domain decoupling strategy is introduced to simulate the physical characteristics of diffuse light fields; building on this, a differentiable inverse spectral gating (DISG) mechanism is utilized to explicitly impose low-pass spectral regularization for precise depth recovery; and further, a dual-domain synergistic module is integrated to dynamically fuse spatial and frequency features, achieving high-fidelity detail preservation. Extensive experiments on the Digimouse benchmark demonstrate that the proposed STAR-Net achieves the highest dice coefficient under single-view conditions, validating that explicit spectral topology modeling is a powerful paradigm for mitigating depth ambiguity.
Federated learning (FL) enables privacy-preserving collaboration for medical image analysis across decentralized institutions but faces major challenges from non-IID data distributions, high communication overhead in multi-round protocols, and catastrophic forgetting when models must adapt to sequentially arriving tasks. These issues are particularly critical in healthcare, where repeated client participation is often infeasible. We address this setting by proposing a novel class-incremental continual learning (CL) model for a one-shot FL paradigm, in which each task introduces new classes, clients observe heterogeneous and evolving class distributions, and communication with the server occurs only once. Clients estimate class-conditional feature distributions via Variational Inference (VI) from private data and transmit compact statistics to the server, which synthesizes features to train a global classifier in a single communication round. The server aggregates these distributional summaries, synthesizes feature embedding, and learns a global classifier without revisiting real past data or contacting clients again. Our major novelty is continual adaptation at the distribution level via synthetic replay from stored class mixtures, complemented by lightweight distillation. This approach substantially mitigates catastrophic forgetting while consistently enhancing recognition of newly introduced classes. Extensive experiments on multiple medical imaging benchmarks demonstrate that our method outperforms both state-of-the-art one-shot FL and class-incremental CL approaches. Compared with existing CL models, it achieves 90–97% average accuracy with near-zero forgetting (≤ 0.10%) across different tasks and evaluation settings.
Self-supervised pre-training has become a key paradigm for reducing annotation costs in 3D medical imaging, yet many recent approaches rely on complex objectives or incur substantial computational overhead. We propose a simple and efficient self-supervised pre-training framework for 3D medical images based on a two-fold patch-wise perturbation strategy. The method applies Bernoulli patch masking and discrete rotations, and trains a shared encoder with a three-head objective for reconstruction, perturbation localization, and rotation prediction. This design encourages spatially aware and transferable representations while remaining computationally lightweight. Experiments across diverse segmentation and classification benchmarks, including modality-shift scenarios, demonstrate consistent improvements over general self-supervised baselines and competitive or superior performance compared to recent medical self-supervised methods, while requiring substantially less memory, computation, and training time than the state-of-the-art pre-training pipelines.
Clinical assessments are often collected using heterogeneous assessment systems across centers and time, leading to records that mix different but related sets of measurements. This motivates harmonization beyond total-score linking. We formulate clinical harmonization at the measurement level as a bidirectional recoverability problem: given paired observations from two assessment systems, the goal is to identify which measurements can be reliably translated in both directions within an application-defined tolerance, while separating non-translatable components. We propose Bidirectional Feature Harmonization Decomposition (BFHD), a feasibility-driven framework that enforces bidirectionally coupled translation and uses feature-wise output gating to produce an explicit decomposition in the original measurement space. Experiments on synthetic data and real clinical assessment pairs show that BFHD achieves broader feasible harmonization coverage and improved subset stability compared to baselines.
Mechanical ventilation (MV) is essential in intensive care units (ICUs), yet conventional protocols lack personalization and risk harmful over- or under-ventilation. Offline reinforcement learning (ORL) enables policy optimization from retrospective clinical data without unsafe online interaction, but existing methods are highly sensitive to distributional shift and out-of-distribution (OOD) actions, limiting their reliability in complex clinical settings. To address these challenges, We propose UBER-CQL (Uncertainty-Balanced Exploration and Robust Conservative Q-Learning), a robust ORL algorithm for safe decision-making under dataset shift. UBER-CQL integrates heteroscedastic Bayesian neural networks with conservative Q-learning to model posterior Q-value uncertainty, which is used to adaptively penalize unreliable high-risk actions while maintaining performance within the data support. We further design numerically stable objectives for conservative Bayesian value estimation. Experiments on in-distribution and OOD subsets of MIMIC-III and eICU demonstrate that UBER-CQL outperforms state-of-the-art ORL and clinician baselines, producing safer and more effective MV strategies.
Clinical prediction increasingly relies on multi-modal inputs, where reliability and efficiency are crucial for real-world deployment. However, mainstream fusion and MoE gating typically treat all available modalities as uniformly beneficial and allow noisy or weakly informative modalities to perturb routing, leading to instability, routing collapse, and miscalibrated confidence under missingness and shift. We propose QA-MoE, a Quality-Aware and stable multimodal Mixture-of-Experts that decouples reliability estimation from routing to enable robust, sparse, and interpretable fusion. QA-MoE adopts a modular architecture where each modality is initially encoded into a shared embedding space. To deal with structurally missing data, we employ a completion pathway that maintains a consistent interface. Unlike standard approaches, QA-MoE separates reliability estimation from the routing process. We propose an Evidential Quality Scorer to measure epistemic uncertainty, which then guides a Stability-Enhanced Subset Selector to filter out noisy modalities on the fly. Additionally, we include a Ternary Expert Aggregation mechanism acting as a specialized branch to stabilize predictions when data missingness is severe. Evaluations on clinical benchmarks (ADNI for Alzheimer’s staging and MIMIC-IV for Length-of-Stay) demonstrate that QA-MoE outperforms strong multimodal baselines, improving reliability while cutting down unnecessary computation. This indicates that QA-MoE offers a robust solution for multimodal decision support, especially in clinical settings prone to noise and missing data.
Healthcare facility visit prediction is essential for optimizing healthcare resource allocation and informing public health policy. Despite advanced machine learning methods being employed for better prediction performance, existing works usually formulate this task as a time-series forecasting problem without considering the intrinsic spatial dependencies of different types of healthcare facilities, and they also fail to provide reliable predictions under abnormal situations such as public emergencies. To advance existing research, we propose HealthMamba, an uncertainty-aware spatiotemporal framework for accurate and reliable healthcare facility visit prediction. HealthMamba comprises three key components: (i) a Unified Spatiotemporal Context Encoder that fuses heterogeneous static and dynamic information, (ii) a novel Graph State Space Model called GraphMamba for hierarchical spatiotemporal modeling, and (iii) a comprehensive uncertainty quantification module integrating three uncertainty quantification mechanisms for reliable prediction. We evaluate HealthMamba on four large-scale real-world datasets from California, New York, Texas, and Florida. Results show HealthMamba achieves around 6.0% improvement in prediction accuracy and 3.5% improvement in uncertainty quantification over state-of-the-art baselines.
Medical image quality assessment (IQA) serves as a critical safeguard for precise clinical diagnosis and treatment. However, existing methods still face challenges arising from data scarcity and heterogeneity across imaging domains, which confine solutions to domain-specific designs and limit their cross-domain generalization ability. In response, we construct a multi-domain and multi-organ dataset comprising 9,105 2D and 3D medical images across three imaging domains and 18 organs, annotated by radiologists. Building upon this, we propose a cross-domain universal medical IQA method termed CDMIQA, which integrates efficient feature extractors with Hierarchical Perceptual Encoding Modules to capture and refine multi-level perceptual features while mitigating interference from noise and artifacts. Notably, the Adaptive Semantic Perception Module is designed to extract semantic features; it enhances adaptability to domain-specific images by dynamically encoding quality variations across different imaging domains and organs. Furthermore, a Top-down Feature Fusion Module is utilized to progressively aggregate features under the guidance of semantic information, reinforcing the model's feature representation capability. Experimental results on the proposed dataset demonstrate that our method outperforms standard competitors and exhibits superior generalization ability across diverse domains. Our code and dataset are available at https://github.com/Leilei-Huang-work/CDMIQA.
Drug repositioning has emerged as an attractive drug development strategy with deep learning-based computational methods showing great potential in predicting Drug-Disease Associations (DDAs). However, dominant computational paradigms typically rely on Random Negative Sampling (RNS) and static embedding fusion, leading to two fundamental limitations. First, RNS treats unobserved pairs uniformly, resulting in coarse decision boundaries that fail to distinguish true associations from ambiguous candidates. Second, static fusion applies a monolithic combination of heterogeneous features, failing to adapt to the sample-specific dominance of different biological mechanisms. To address these issues, we propose DIAM, which establishes a mechanism-adaptive paradigm by explicitly decoupling structural and molecular signals. Specifically, DIAM introduces a Dual-Stream Biological Mechanism Decoupling module to construct global structural propagation and local molecular interaction views explicitly. Leveraging these views, we design a biological plausibility score to guide the hard negative sampling, enforcing finer-grained decision boundaries. Furthermore, an Adaptive Residual Gating (ARG) is devised to perform instance-aware modulation, dynamically weighing the contribution of global and local views for each specific pair. Extensive experiments on three benchmark datasets demonstrate that DIAM outperforms seven state-of-the-art methods. A case study on Alzheimer's disease further validates the model's effectiveness in identifying potential candidate drugs for practical application.
Spatio-temporal epidemic forecasting is critical for public health management, yet existing methods often struggle with insensitivity to weak epidemic signals, over-simplified spatial relations, and unstable parameter estimation. To address these challenges, we propose the Spatio-Temporal priOr-aware Epidemic Predictor (STOEP), a novel hybrid framework that integrates implicit spatio-temporal priors and explicit expert priors. STOEP consists of three key components: (1) Case-aware Adjacency Learning (CAL), which dynamically adjusts mobility-based regional dependencies using historical infection patterns; (2) Space-informed Parameter Estimating (SPE), which employs learnable spatial priors to amplify weak epidemic signals; and (3) Filter-based Mechanistic Forecasting (FMF), which uses an expert-guided adaptive thresholding strategy to regularize epidemic parameters. Extensive experiments on real-world COVID-19 and influenza datasets demonstrate that STOEP outperforms the best baseline by 11.1% in RMSE. The system has been deployed at a provincial CDC in China to facilitate downstream applications.
CT-to-PET synthesis aims to synthesize PET images from the widely available and lower-cost CT scans to address the high cost and additional radiation exposure associated with PET scanning. However, CT-to-PET synthesis faces two key challenges due to the sequential correlation of volumetric imaging: preserving smooth transition between adjacent slices and ensuring style consistency across long-range slices. To overcome these limitations, we propose the Style-aware Bidirectional Stream Diffusion Model, which ensures both inter-slice continuity and global style consistency with low computational cost. Specifically, we first utilize a Vector Quantized Variational Autoencoder to encode bidirectional adjacent CT slices into latent codes. A Neighbor Attention module is then introduced to capture transition patterns among these latent codes, ensuring structural continuity. To enhance style consistency, we further design a Prototype Prompting mechanism to construct a feature pool from long-range slices. Global style prototypes are extracted from the feature pool and dynamically integrated into the generation process, guiding the model’s attention toward consistent stylistic features across slices. Extensive experiments demonstrate that our approach significantly outperforms state-of-the-art methods.