NeurIPS 2026 论文列表

Electromagnetic inverse scattering (EIS), which aims to recover object permittivity from measured scattered fields, is central to a wide range of applications, from medical diagnostics to security screening. However, it is a highly nonlinear and ill-posed problem. Despite substantial progress, existing approaches face a fundamental trade-off between physical fidelity, efficiency, and generalization: data-driven methods are fast but tied to training geometries and sensor setups, while physics-based optimization is accurate but computationally expensive. We introduce a Gaussian-Galerkin (GauGal) method that reformulates EIS from a dense point-wise field reconstruction to a compact, primitive-level physical solving. Rather than using Gaussian primitive merely as a material representation, GauGal take it as the computational units for wave scattering. By projecting the continuous EIS scattering equation into a Gaussian primitive space, the material modulation, Green propagation, source excitation, and receiver observation are all realized as primitive-level operators. This yields a compact differentiable forward solver for physics-consistent reconstruction. Our method achieves state-of-the-art accuracy on synthetic and real benchmarks while reducing runtime from over 30 minutes by leading physics-driven baselines to under 10 seconds. It also generalizes significantly better to unseen geometries and sensor configurations than leading data-driven baselines. Overall, this framework establishes an accurate, efficient, and generalizable paradigm for electromagnetic inverse imaging, enabling fast, physics-consistent imaging in practical settings.

Explainability, the capability of an artificial intelligence system (AIS) to explain its outcomes in a manner that is comprehensible to human beings at an acceptable level, has been deemed essential for critical sectors, such as healthcare. Is it really the case? In this perspective, we consider two extreme cases, Oracle (without explainability) versus AI Colleague (with explainability) for a thorough analysis. We discuss how the level of automation and explainability of AIS can affect the determination of liability among the medical practitioner/facility and manufacturer of AIS. We argue that explainability plays a crucial role in setting a responsibility framework in healthcare, from a legal standpoint, to shape the behavior of all involved parties and mitigate the risk of potential defensive medicine practices.

Real-world scenes often contain static, slow, and fast components within the same field of view. Frame cameras are inherently inefficient in this setting, as their global exposure and fixed sampling must follow the fastest motion and therefore oversample the rest of the scene. Event cameras provide asynchronous local sensing, but standard sensors use a single global contrast threshold, leading to a trade-off between sensitivity and redundancy: low thresholds preserve static information but over-trigger under fast motion, while high thresholds reduce data volume but lose slow or static structures. We propose a multi-threshold event imaging model that assigns motion-compatible thresholds to different scene components, enabling faithful multi-velocity-scale imaging with fewer events. We also introduce a practical acquisition pipeline that implements this model on existing event cameras. Experiments on synthetic and real scenes validate the effectiveness of our approach in preserving both static structures and fast dynamics while reducing data volume.

Entropy minimization (EM) is the dominant objective for test-time adaptation, yet its failure mode, model collapse, remains poorly understood. In this work, we show that distribution shifts can cause feature clusters corresponding to distinct classes in the model’s representation space to merge, while the decision boundary remains fixed. This induces a systematic skew in the predicted class distribution, referred to as prediction bias. Prediction bias refers to a shift in the predicted class distribution, with some classes overrepresented and others suppressed. We show that entropy minimization amplifies this prediction bias by tightening the existing clusters, reinforcing the incorrect groupings until all predictions collapse to a trivial solution. Next, to demonstrate the significance of prediction bias and mitigate it, we further propose Distribution Shift Bias Reduction (DSBR), a bias-correcting objective that specifically targets this failure mode by equalizing the contribution of each predicted class to the unsupervised entropy minimization loss. To study this failure mode, we design suitable adaptation settings using four medical‑imaging datasets and additionally evaluate on ImageNet‑C. We find that DSBR consistently stabilizes test‑time adaptation, prevents model collapse, and matches or outperforms the state‑of‑the‑art methods. Moreover, DSBR operates solely at test-time.

Medical vision-language models (VLMs) have shown increasing potential for clinical image interpretation, including lesion detection and report generation. However, their practical utility remains limited by insufficient sensitivity to subtle lesions, whose visual evidence is often sparse, low-contrast, and embedded within complex anatomical context. As local visual tokens are aggregated, these weak lesion cues can become underrepresented in global image representations, making them difficult for medical VLMs to recognize. Existing efforts to improve lesion sensitivity mainly rely on medical-domain vision-encoder pre-training, clinical-term-guided alignment, or trainable pathological representation enhancement. Although effective, these approaches usually require additional training or model-specific adaptation and may overfit to particular disease morphologies, limiting their applicability to frozen medical VLMs. To address these limitations, we propose \textbf{EasyLens}, a training-free plug-and-play subtle-lesion representation amplifier for medical VLMs. EasyLens first constructs \textbf{EasyBank}, a pathology-anatomy prototype space that provides lesion-related prototypes and anatomy-aware normal references for comparing suspicious patches against both pathological and normal anatomical patterns. To avoid blindly amplifying normal tissues, \textbf{EasyTag} selects lesion-relevant patches through counterfactual prototype reasoning. To counteract the dilution of subtle lesion cues in global image representations, \textbf{EasyAmplifier} strengthens the selected lesion-relevant patch representations through morphology-guided residual enhancement, thereby increasing their contribution to the global image embedding. Experiments on multiple medical image datasets and frozen medical VLM backbones show that EasyLens consistently improves subtle-lesion detection and outperforms existing encoder-enhancement baselines without model fine-tuning. Code is available at: https://anonymous.4open.science/r/easylens-BEC2

Federated Domain Generalization (FDG) aims to learn a global model robust to heterogeneous domain shifts without sharing raw data, a critical challenge in multi-center medical imaging. Most existing methods implicitly assume static inter-domain discrepancies, overlooking the fact that client representations evolve continuously during local optimization. We identify this phenomenon as representation dynamics, which refers to the temporal evolution of latent features induced by training updates rather than shifts in underlying data distributions. Such dynamics often lead to unstable aggregation, particularly in heterogeneous medical imaging scenarios. To address this issue, we propose Dynamic Knowledge Tracked FDG (DKT-FDG), a framework that explicitly tracks these dynamics by leveraging flow matching to model continuous representation trajectories. By aligning representation evolution instead of static snapshots, DKT-FDG improves robustness to domain shifts. Comprehensive experiments on multiple multi-center medical benchmarks demonstrate consistent improvements over state-of-the-art FDG methods, highlighting the importance of modeling representation dynamics in federated learning.

The development of deep learning-based medical AI depends on high-quality annotated data. Yet, dataset creation remains constrained by the need for expert radiologist annotations, limiting curated datasets to relatively small cohorts. To overcome this limitation, recent efforts increasingly aggregate disparate public datasets into large-scale dataset collections. However, combining datasets at scale often obscures data provenance, introducing significant risks of reuse propagation, unintended leakage, bias amplification and distorted data distributions. In this work, we study these problems on a massive collection of 760k 3D radiological images across 1069 public datasets and provide practical resources to address it. Firstly, we formalize dataset collections as a distinct paradigm in medical image analysis and characterize their structural challenges, including reuse propagation, duplication, and provenance fragmentation. Secondly, we perform one of the first large-scale empirical studies of duplication on medical image collections and demonstrate extensive exact and near-duplicate reuse across public datasets via perceptual hashing and manual tracing on our 760k 3D radiological images across 1069 public datasets, with 118k near or exact duplicates. We make these hashes publicly available, thereby providing a standardized reference for identifying potential overlaps and shared provenance for researchers. Thirdly, owing to the insufficient robustness of hashing in isolation, we introduce MAP (Metadata for Aggregation and Provenance), a lightweight, portable metadata profile for documenting provenance, known overlaps, and deduplication decisions in dataset collections. Our contributions enable dataset collections to shift from coarse aggregations to reliable and provenance-aware data scaling mechanisms in medical image analysis. A webtool supporting our work is made available here:

Learning to Defer (L2D) enables a model to predict autonomously or defer to an expert, but prior work largely assumes flat label spaces. We study the first L2D setting with hierarchical multi-label decisions, motivated by medical-imaging workflows in which findings are organised by clinical taxonomies. In this setting, deferral is a delegation action rather than a label assignment, so treating it as an independent per-label decision can produce \emph{deferral incoherence}, including taxonomic contradictions, delegation violations, and deferrals of labels already implied by the model's own assertions. We formalise coherent hierarchical deferral under a Selective-Exclusion handoff contract, characterise the Bayes-optimal coherent deferral rule, and show that even nodewise Bayes L2D can be action-incoherent. We then propose two remedies: exact coherent projection, a dynamic-programming decoder over the coherent action set, and Taxonomic Belief Propagation (TBP) with Recursive Policy Optimisation (RPO), a contract-aware joint action model trained through the same recursion used at inference. Across real-reader and controlled-expert medical-imaging benchmarks, naïve binary-relevance L2D exhibits non-trivial incoherence. Projection removes it exactly, and fast TBP+RPO drives incoherence near zero while retaining strong utility.

Deep models for medical time series can achieve strong benchmark accuracy yet remain brittle under acquisition artifacts, physiological variability, and low-resource training conditions. We present \textbf{ClinStab}, a Medformer-based framework for EEG and ECG classification that studies whether channel-aware dual-stream interaction and perturbation-aware optimization can improve the clean/robustness tradeoff. The proposed \textbf{S}alience-\textbf{C}ontext \textbf{D}ual \textbf{M}odulation (SCDM) module uses energy as a routing prior: high-energy channel-aligned components receive attentive modeling, while residual components remain trainable through a lightweight context pathway. ClinStab then combines clean supervision, perturbed supervision, prediction consistency, and teacher-guided stabilization under adversarial perturbations. Across four public datasets and 11 baselines, ClinStab improves accuracy over Medformer on all datasets and improves selected robustness metrics under the perturbation settings studied here, while exposing tradeoffs on PTB ranking metrics and soft-routing alternatives. These results support ClinStab as a practical same-backbone stability intervention, not as evidence of universal clinical robustness.

Reinforcement learning (RL) is a promising way to improve the clinical reasoning abilities of large language models (LLMs), but current pipelines often rely on expert-labeled questions or costly synthetic-data generation. Meanwhile, the medical literature offers a wealth of high-quality unlabeled documents, including clinical case reports that trace the full clinical course from initial presentation to final treatment decisions. We study whether document-grounded self-play can convert these reports into effective RL training signals. We identify two challenges that limit existing self-play methods in this setting: knowledge-point fixation, where the proposer repeatedly asks about a narrow set of salient clues in a source report, and difficulty-quality mismatch, where appropriately difficult questions may still be poorly formed or shallow, limiting their value for reasoner training. To address these challenges, we propose CasePlay, a self-play framework with two key components: (a) a Knowledge-Conditioned Proposer, which anchors question generation to case-specific knowledge points so that questions draw on a broader range of evidence from the source report; and (b) a Rubric-Judge Reasoner, which augments answer-accuracy feedback with rubric-based quality feedback on clue integration, reasoning depth, and option quality, guiding the proposer toward reasoning-intensive questions that better support reasoner training. Extensive experiments across seven medically related benchmarks demonstrate that CasePlay outperforms existing self-play baselines and improves the SFT warm-start model by 3.0 points on average, highlighted by a 6.9-point gain on LiveClin. This approach offers an effective path toward continually improving medical reasoning ability through self-play RL. Code is available at \url{https://anonymous.4open.science/r/CasePlay}.

Multimodal Large Language Models (MLLMs) have shown strong performance on medical public benchmarks, but existing evaluations remain limited as clinically grounded assessments because they often rely on isolated inputs lacking study-level structure and simplified, recognition-style tasks that are far from real diagnostic workflows. We introduce \textbf{CardioLens}, a leakage-resistant evaluation testbed using multi-sequence Cardiovascular Magnetic Resonance (CMR), constructed from private hospital archives via a rigorous report-to-QA construction and verification pipeline. CardioLens is built from 473,896 slices and 13,494 verified QA pairs across 4D Cine, LGE, perfusion, and T2-weighted imaging, and evaluates three stages of CMR interpretation: image understanding, report generation, and disease diagnosis. Across 24 state-of-the-art MLLMs, CardioLens reveals a substantial clinical reality gap. Models perform poorly overall, with performance degrading along the real CMR workflow. Confusion analysis further reveals a category-collapse failure mode: models often default to frequent abnormal categories rather than reliably telling distinct clinical findings. To rule out MLLM-compatible input construction as the failure cause, we compare random, clinically-motivated, and data-driven selection protocols under different slice budgets; performance changes only marginally, typically by about 1\%. Explicit reasoning prompts also fail to rescue performance, often driving models toward conservative predictions rather than improving visual evidence use. These results show that current MLLMs remain far from reliable CMR interpretation, where clinical decisions require integrating distributed evidence across sequences, views, and temporal phases. By exposing where and how current MLLMs fall short, CardioLens provides a clinically grounded testbed for developing the next generation of MLLMs toward real-world clinical depolyment.

As vision language models are increasingly deployed in clinical diagnosis, understanding how they internally resolve competing visual and textual signals becomes a safety imperative. Existing mechanistic analyses remain confined to unimodal text and offer no explanation for why a single misleading sentence can override a correct image based diagnosis, or why a model commits to a confident answer despite insufficient visual evidence. We find that these two safety risks, arbitration failure where textual context overrides visual grounding and brake failure where the model commits without adequate evidence, are mediated by spatially disjoint attention head populations: arbitration heads form a mid-to-deep wideband reflecting cross-layer evidence competition, while brake heads concentrate in a narrow middle-to-late layer band that regulates evidence sufficiency and abstention behavior. To ground these observations in causal circuitry, we introduce CRAFT, which localizes each failure mode to a minimal causal head set via dual criteria and verifies necessity and sufficiency through temporal probes and Tuned Lens trajectory analysis. Excising arbitration heads sharply reduces conflict following with negligible degradation on clean inputs, while excising brake heads restores appropriate abstention under degraded visual evidence. The two interventions target spatially disjoint head sets and produce distinct corrective effects, underscoring the mechanistic separability of the failure modes. Experiments across multiple medical VQA benchmarks and VLM architectures validate both the localization and interventions, demonstrating that the identified heads causally drive each failure mode and that targeted modulation generalises without retraining. The code is available at \url{https://anonymous.4open.science/r/CRAFT-B3CA}.

Multiple instance learning (MIL) is the dominant framework for whole-slide image analysis in computational pathology, typically combining a frozen patch encoder, a projection layer, and a slide-level aggregator. While encoders and aggregators have been extensively studied, the projection layer remains a largely morphology-only bottleneck. This limits endpoints such as biomarker status and survival, which are governed by a molecular state that is not fully captured by H\&E morphology. We introduce Molecularly Informed Staining Transform (MIST), a plug-in replacement for the MIL projection layer that uses paired spatial transcriptomics only during training to construct virtual molecular stains. MIST clusters gene expression profiles into cross-modal prototypes, anchors them in the frozen foundation model feature space, and uses them to reorganize H\&E patch features along molecularly guided axes. It requires no transcriptomics at inference and can be inserted before standard MIL aggregators. We evaluate MIST across 23 downstream tasks and 8 MIL aggregators. MIST improves 240 of 256 configurations over the standard projection layer, with an average gain of +3.5\%, observed consistently across endpoint types: +5.2\% on survival prediction, +3.3\% on tissue subtyping, and +2.6\% on biomarker prediction. Ablations confirm that gene-derived prototypes are the primary source of the gains, while spatial, biological, and pathological analyses show that cross-modal prototype affinities capture spatially coherent molecular programs from H\&E alone.

Plug-and-Play (PnP) methods have become standard tools for solving imaging inverse problems by replacing the intractable maximum a posteriori (MAP) denoiser with the MMSE one. While this mismatch has been widely treated as unavoidable, recent works have sought to close this gap by targeting the MAP with diffusion-model scores. We show this is problematic in practice: learned scores do not match the true ones, so MAP-targeting iterations converge to cartoon-like images rather than realistic ones, and better results are obtained by stopping short of convergence. We turn this observation into a design principle and introduce $\texttt{ProxiMAP}$, an iterative MAP approximation whose noise schedule keeps the iterate's residual noise matched to the denoiser's training noise. This keeps the denoiser in-distribution where its score is reliable, and yields implicit early stopping that avoids the failure mode above. $\texttt{ProxiMAP}$ is a modular drop-in replacement for MMSE denoisers in standard PnP algorithms and consistently sharpens reconstructions across deblurring, inpainting, super-resolution, and phase retrieval. Building on the same principle, we propose a hybrid variant that applies $\texttt{ProxiMAP}$ only in the late iterations of PnP, where the denoiser is most reliable—matching or exceeding the full-replacement variant at a fraction of the cost.

Cross-architecture/backbone medical-segmentation pools are commonly motivated as complementary failure coverage when members make different mistakes; that assumption is rarely tested at the per-case level. We audit this assumption for a fixed pool of 11 pretrained-encoder bundles read out by a deliberately weak $1\times1$-convolution decoder (4 seeds), and for a small set of decoder-recipe perturbations on the same bundles. Across five primary task rows (Kvasir polyp, ACDC LV, BraTS foreground, RIGA Cup, RIGA Disc), this 11-bundle $1\times1$-conv readout pool yields per-case Dice correlation gaps of $\Delta=0.261\text{--}0.638$ between same-bundle and cross-bundle pairs after a Dice $\geq 0.30$ functional floor; all paired case and hierarchical bootstrap intervals exclude zero. The result is stable under floor sweeps, ICC(A,1), leave-one-out, subject aggregation, quality-controlled pair regression, and cross-fitted item-difficulty residualisation. Decoder-recipe diagnostics show that richer readouts attenuate the gap (UNet-skip; case-identical nnU-Net on RIGA Cup: $0.473\to 0.0079$, separate-split $0.034\text{--}0.047$); the reported magnitudes are recipe-specific. Additional probes (pixel error, joint-failure lift, same-architecture-family ViT, same-family cross-checkpoint, same-backbone) locate the dependence structure but do not causally separate architecture, pretraining, and recipe. The claim is scoped to 2D frozen/light-adaptation encoder pools with this readout family and to retrospective benchmark audits. We release per-case Dice traces, summary JSONs, scorer code, hashes, and metadata as the audit artifact.

Medical Vision-Language Pre-training (VLP) leverages the semantic richness of radiology reports to learn 3D representations, yet current architectures remain fundamentally anatomy-agnostic. While clinical diagnostics rely on organ-specific context, standard encoders apply a uniform, dense parameterization to all volumetric patches, and existing Mixture-of-Experts (MoE) routers gate on isolated local features without explicit access to the organ composition of the scan or crop. We propose Anatomy-Activated Mixture-of-Experts (A$^{2}$MoE), a framework that incorporates anatomical inductive biases within the model's computational path. A$^{2}$MoE introduces two key components: (1) a Histogram Representation Router (HRR) that conditions expert selection on global visual context and a predicted anatomy-presence histogram, ensuring that parameters specialize in specific anatomical domains; and (2) a mask-free Organ-Query Multi-Scale Projector (OQMSP) that utilizes learnable queries to extract organ-level embeddings for fine-grained alignment without requiring manual annotations at inference. By routing at the patch level rather than the token level, A$^{2}$MoE reduces routing complexity by orders of magnitude while satisfying load balancing by construction. Pre-trained on over 60,000 multi-phase CT scans, A$^{2}$MoE achieves state-of-the-art performance across segmentation, classification, and detection benchmarks. Furthermore, we demonstrate that our anatomically-grounded inductive biases support robust generalization to MRI and ultrasound, establishing a scalable foundation for medical representation learning.

LLMs-as-judges (LLJs) are increasingly used to reduce the cost of expert review in medical evaluation. However, high alignment with clinicians does not by itself establish validity or safety. This position paper argues that current reporting practices for medical LLJs are insufficient: LLJ-clinician alignment should not be reported as standalone evidence that a judge is reliable, clinically grounded, or safe for downstream use. We identify four risks: unstable evaluations across prompts, runs, and judge models; opaque scores that hide clinically meaningful errors; weak validity under clinical uncertainty and limited clinician agreement; and downstream safety or data contamination risks when flawed scores influence model selection or synthetic data filtering. By re-examining LLJ evaluation setups used in prior medical studies, we show that judges treated as practical evaluators in the literature can still reject clinically equivalent diagnoses, overrate incomplete or unsafe answers, reward unsupported clinical notes, and penalize appropriate treatment plans. We propose a protocol for medical LLJ evaluation that requires task-specific codebooks, expert-annotated validation sets, codebook-derived prompts, robustness testing, clinical error analysis, uncertainty reporting, and transparent disclosure before LLJ scores are used for medical benchmarking or decision support.

Radiologist eye-tracking data provide a rich record of how experts search, compare, and accumulate evidence during image reading, yet existing methods exploit this signal only partially, either as a static spatial prior or as an auxiliary prediction target decoupled from diagnosis. We propose GazeWorld, a medical imaging world model that treats the image as the world and the radiologist's fixation sequence as a trajectory through it. GazeWorld autoregressively predicts the latent representation of the next fixated patch from all previously visited ones, while a spatial-completion branch covers unvisited regions. At inference, GazeWorld generates a sequence of patch representations from the image alone without requiring real gaze data. Frozen GazeWorld features achieve state-of-the-art diagnostic accuracy across all nine supervised settings on CheXpert, RSNA Pneumonia, and SIIM-ACR Pneumothorax, as well as the highest zero-shot accuracy on all three benchmarks. On the GazeSearch benchmark, a generic decoder trained on the same frozen features outperforms the purpose-built LogitGaze-Med by over 16\% in ScanMatch and 22\% in SED, despite not being explicitly trained to predict gaze. GazeWorld demonstrates that modeling how experts read, not just what they conclude, offers a promising pretraining paradigm for medical imaging AI.