AAAI2026 Medical Imaging AI paper notes paper summaries Segmentation Alignment/RLHF Multimodal/VLM Adversarial Robustness LLM

🏥 Medical Imaging¶

🤖 AAAI2026 · 75 paper notes

📌 Same area in other venues: 📷 CVPR2026 (163) · 🔬 ICLR2026 (88) · 🧪 ICML2026 (28) · 🧠 NeurIPS2025 (74) · 📹 ICCV2025 (31)

🔥 Top topics: Medical Imaging ×35 · Segmentation ×11 · Alignment/RLHF ×7 · Multimodal/VLM ×6 · Adversarial Robustness ×4

A Disease-Aware Dual-Stage Framework for Chest X-ray Report Generation: This paper proposes a two-stage disease-aware framework that learns 14 Disease-Aware Semantic Tokens (DASTs) corresponding to pathology categories for explicit disease representation. It further employs a Disease-Visual Attention Fusion (DVAF) module and a Dual-Modal Similarity Retrieval (DMSR) mechanism to assist an LLM in generating clinically accurate chest X-ray reports, achieving state-of-the-art performance on three datasets: CheXpert Plus, IU X-Ray, and MIMIC-CXR.
Advancing Safe Mechanical Ventilation Using Offline RL With Hybrid Actions and Clinically Aligned Rewards: This paper addresses the problem of optimizing mechanical ventilation (MV) settings in the ICU via offline RL. A hybrid action space approach (HybridIQL/HybridEDAC) is proposed to avoid distributional shift caused by conventional discretization. Clinically aligned reward functions are introduced based on ventilator-free days (VFD) and physiological safety ranges, with multi-objective optimization used to select the optimal reward. The number of optimizable ventilation parameters is scaled from 2–3 to 6, and HybridIQL achieves the best balance between performance and policy coverage.
Ambiguity-aware Truncated Flow Matching for Ambiguous Medical Image Segmentation: This paper proposes the ATFM framework, which decouples prediction accuracy and diversity into distribution-level and sample-level optimization through a data-hierarchical inference paradigm. By integrating two modules — Gaussian Truncation Representation (GTR) and Segmentation Flow Matching (SFM) — ATFM simultaneously improves prediction accuracy, fidelity, and diversity in ambiguous medical image segmentation.
Bayesian Meta-Analyses Could Be More: A Case Study in Trial of Labor After a Cesarean-section Outcomes and Complications: This paper proposes a hierarchical Bayesian meta-analysis framework that models the unrecorded clinical decision variable (Bishop score) as a truncated latent variable, correcting the biased conclusions arising from omitted confounders in conventional fixed-effect meta-analyses. Applied to the TOLAC (Trial of Labor After Cesarean) setting, the method demonstrates no significant difference between mechanical dilation and Pitocin.
Bidirectional Channel-selective Semantic Interaction for Semi-Supervised Medical Segmentation: This paper proposes the BCSI framework, which employs a channel-selection router to dynamically identify critical feature channels and performs bidirectional channel-level interaction between labeled and unlabeled data streams. Combined with semantic-spatial perturbation-based weak-to-strong consistency learning, BCSI achieves substantial improvements in semi-supervised medical image segmentation.
Bridging Vision and Language for Robust Context-Aware Surgical Point Tracking: The VL-SurgPT Dataset and Benchmark: This paper presents VL-SurgPT, the first large-scale multimodal surgical point tracking dataset combining visual coordinates with textual state descriptions, and proposes TG-SurgPT, a text-guided tracking method that leverages semantic information to significantly improve tracking accuracy and robustness in complex surgical scenes.
CAT-Net: A Cross-Attention Tone Network for Cross-Subject EEG-EMG Fusion Tone Decoding: This paper proposes CAT-Net (Cross-Attention Tone Network), which achieves Mandarin four-tone classification using only 20 EEG channels and 5 EMG channels via spatial-temporal feature extraction branches, a cross-attention fusion mechanism, and domain adversarial training. The model achieves 87.83%/88.08% accuracy under voiced/silent speech conditions and 83.27%/85.10% under cross-subject evaluation, outperforming all 8 baseline methods.
CD-DPE: Dual-Prompt Expert Network Based on Convolutional Dictionary Feature Decoupling for Multi-Contrast MRI Super-Resolution: This paper proposes CD-DPE, a network that employs an iterative Convolutional Dictionary Feature Decoupling Module (CD-FDM) to disentangle multi-contrast MRI features into cross-contrast shared and modality-specific components, followed by a Dual-Prompt Feature Fusion Expert Module (DP-FFEM) for adaptive fusion and reconstruction. CD-DPE surpasses existing state-of-the-art methods on multiple public benchmarks.
Coarse-to-Fine Open-Set Graph Node Classification with Large Language Models: This paper proposes the Coarse-to-Fine Classification (CFC) framework, which leverages the zero-shot reasoning capability of LLMs to supply semantically grounded OOD samples and a potential OOD label space for open-set graph node classification, enabling the model not only to detect OOD nodes but also to classify them into specific unknown categories.
CoCoLIT: ControlNet-Conditioned Latent Image Translation for MRI to Amyloid PET Synthesis: This paper proposes CoCoLIT, a ControlNet-conditioned latent diffusion framework for synthesizing amyloid PET images from structural MRI. Through a Weighted Image Space Loss (WISL) and Latent Averaging Stabilization (LAS), CoCoLIT substantially outperforms existing methods.
Cross-Sample Augmented Test-Time Adaptation for Personalized Intraoperative Hypotension Prediction: This paper proposes the CSA-TTA framework, which enhances personalized intraoperative hypotension prediction at test time by constructing a cross-sample bank, performing coarse-to-fine retrieval, and applying multi-task optimization to retrieve hypotension event signals from other patients' data.
Decoding with Structured Awareness: Integrating Directional, Frequency-Spatial, and Structural Attention for Medical Image Segmentation: This paper proposes a novel decoder framework for medical image segmentation comprising three modules: Adaptive Cross-Fusion Attention (ACFA) for directional awareness, Triple Feature Fusion Attention (TFFA) for spatial-frequency-wavelet fusion, and Structural-aware Multi-scale Masking Module (SMMM), achieving state-of-the-art performance across multiple benchmark datasets.
DeepGB-TB: A Risk-Balanced Cross-Attention Gradient-Boosted Convolutional Network for Rapid, Interpretable Tuberculosis Screening: This paper proposes DeepGB-TB, a multimodal TB screening system combining a lightweight 1D-CNN (for cough audio) and gradient-boosted decision trees (for demographic features). A bidirectional cross-attention module (CM-BCA) fuses heterogeneous data by mimicking clinical reasoning, while a tuberculosis risk-balanced loss (TRBL) minimizes missed diagnoses. The system achieves AUROC 0.903 on a 7-country dataset and supports offline real-time inference on mobile devices.
DeNAS-ViT: Data Efficient NAS-Optimized Vision Transformer for Ultrasound Image Segmentation: DeNAS-ViT is proposed as the first method to apply NAS at the token level within ViT for optimizing multi-scale feature extraction in ultrasound image segmentation. A NAS-constrained semi-supervised learning framework is designed incorporating network independence loss, hierarchical contrastive loss, and staged optimization, achieving state-of-the-art performance under limited annotation.
DiA-gnostic VLVAE: Disentangled Alignment-Constrained Vision Language Variational AutoEncoder for Robust Radiology Reporting with Missing Modalities: This paper proposes DiA-gnostic VLVAE, a vision-language mixture-of-experts VAE that learns a three-factor latent space (\(Z_v\) visual-specific / \(Z_l\) language-specific / \(Z_s\) shared), with dual constraints of orthogonality and contrastive alignment for disentanglement. The model generates reliable radiology reports even when clinical context is absent, achieving competitive BLEU@4 on IU X-Ray and MIMIC-CXR.
Divide, Conquer and Unite: Hierarchical Style-Recalibrated Prototype Alignment for Federated Medical Segmentation: To address the two key challenges in federated medical image segmentation — layerwise style bias accumulation and incomplete contextual representation — this paper proposes FedBCS: a framework that constructs domain-invariant prototypes via Frequency-domain adaptive Style Recalibration (FSR) and designs Context-aware Dual-level Prototype Alignment (CDPA) to fuse multi-level semantics from both encoder and decoder. FedBCS achieves state-of-the-art performance on nuclei segmentation and prostate MRI segmentation tasks.
Do Large Language Models Think Like the Brain? Sentence-Level Evidences from Layer-Wise Embeddings and fMRI: This paper systematically investigates sentence-level alignment between 14 open-source LLMs and human brain language processing by comparing layer-wise LLM representations with fMRI data recorded while participants listened to a natural narrative. Key findings include: middle layers yield the highest brain alignment, instruction tuning substantially enhances alignment, and hemispheric lateralization patterns consistent with classical neurolinguistic theories are observed.
DualFete: Revisiting Teacher-Student Interactions from a Feedback Perspective for Semi-supervised Medical Image Segmentation: A feedback mechanism is introduced into the teacher-student semi-supervised learning framework, enabling the student to feed back to the teacher information on whether pseudo-label-guided updates are consistent with the direction of supervision from labeled data. This feedback dynamic is further enhanced within a dual-teacher architecture, effectively suppressing error accumulation and confirmation bias in medical image segmentation.
DW-DGAT: Dynamically Weighted Dual Graph Attention Network for Neurodegenerative Disease Diagnosis: To address three core challenges in early diagnosis of neurodegenerative diseases (PD/AD)—multi-indicator data fusion, heterogeneous information extraction, and class imbalance—this paper proposes DW-DGAT, a dynamically weighted dual graph attention network. By introducing a universal data fusion strategy, micro-macro dual-level graph feature learning, and a dynamic class weight generation mechanism, DW-DGAT substantially outperforms 14 baseline methods on the PPMI and ADNI3 datasets.
EgoEMS: A High-Fidelity Multimodal Egocentric Dataset for Cognitive Assistance in Emergency Medical Services: This paper presents the first high-fidelity multi-person multimodal egocentric EMS dataset, comprising 233 trials with 20 hours of video, annotations covering 9 interventions and 67 critical steps, and three benchmark tasks (step classification / online segmentation / CPR quality estimation) to advance the development of cognitive assistance systems for EMS.
Experience with Single Domain Generalization in Real World Medical Imaging Deployments: This paper proposes the DL+EKE framework, which integrates domain-invariant expert knowledge with deep learning to address rare class single domain generalization (SDG) in medical imaging. The approach significantly outperforms state-of-the-art SDG methods across three real-world deployment scenarios: diabetic retinopathy (DR) grading, resting-state fMRI seizure onset zone (SOZ) localization, and stress ECG-based coronary artery disease (CAD) detection.
FaNe: Towards Fine-Grained Cross-Modal Contrast with False-Negative Reduction and Text-Conditioned Sparse Attention: FaNe proposes a semantics-enhanced medical vision-language pre-training framework that addresses the false-negative problem and insufficient coarse-grained alignment in medical VLP through semantics-aware positive mining, text-conditioned sparse attention pooling, and hard-negative-aware contrastive loss.
FIA-Edit: Frequency-Interactive Attention for Efficient and High-Fidelity Inversion-Free Text-Guided Image Editing: This paper proposes FIA-Edit, an inversion-free text-guided image editing framework based on frequency-interactive attention. It introduces a Frequency Representation Interaction (FRI) module that performs frequency-domain fusion of source/target features within self-attention, and a Feature Injection (FIJ) module that explicitly incorporates source image features into cross-attention. The framework achieves precise semantic editing while maintaining high background fidelity, and for the first time applies a general image editing method to clinical surgical bleeding image augmentation.
Fine-Tuned LLMs Know They Don't Know: A Parameter-Efficient Approach to Recovering Honesty: This paper reveals that the root cause of SFT-induced dishonesty in LLMs is impaired self-expression (rather than degraded self-knowledge), and proposes the HCNR framework accordingly. By identifying honesty-critical neurons via Fisher information and restoring them to their pre-trained states with Hessian-guided compensation, HCNR recovers 33.25% of honesty using only 256 data samples and 20% of parameters, achieving over 2.23× speedup.
From Policy to Logic for Efficient and Interpretable Coverage Assessment: This paper proposes a neuro-symbolic approach that combines a coverage-aware retriever with symbolic rule inference based on PyKnow, assisting human reviewers in efficiently and interpretably assessing whether medical CPT codes are covered by insurance policies. The approach reduces inference cost by 44% while improving F1 by 4.5%.
FunKAN: Functional Kolmogorov-Arnold Network for Medical Image Enhancement and Segmentation: This paper generalizes the Kolmogorov-Arnold representation theorem from finite-dimensional scalar spaces to function spaces (Hilbert spaces), proposing the FunKAN framework. By learning inner functions via Fourier expansion over Hermite basis functions, the framework preserves the spatial structure of image data and outperforms existing KAN variants on MRI enhancement and three medical image segmentation tasks.
G2L: From Giga-Scale to Cancer-Specific Large-Scale Pathology Foundation Models via Efficient Fine-Tuning: This paper proposes G2L (Giga-to-Large), a distillation framework that transfers knowledge from a 1.9B-parameter giga-scale pathology foundation model (H-optimus-0) to a 300M-parameter large-scale model (Hibou-L) using only 1K whole slide images, achieving performance on par with or superior to the teacher model and larger models across multiple cancer-specific downstream tasks.
GIIM: Graph-based Learning of Inter- and Intra-view Dependencies for Multi-view Medical Image Diagnosis: This paper proposes GIIM, a Multi-Heterogeneous Graph (MHG)-based framework that simultaneously models intra-view dependencies among lesions and inter-view dynamic variations via graph structures. Four missing-view representation strategies are introduced. GIIM achieves consistent and significant improvements over existing multi-view methods across three imaging modalities: liver CT, breast X-ray, and breast MRI.
Graph-Theoretic Consistency for Robust and Topology-Aware Semi-Supervised Histopathology Segmentation: This paper proposes TGC (Topology Graph Consistency), a framework that introduces graph-theoretic topological constraints by aligning the Laplacian spectra, connected component counts, and adjacency statistics between prediction graphs and reference graphs. TGC achieves near-fully-supervised histopathology segmentation performance using only 5–10% of labeled data.
GROVER: Graph-guided Representation of Omics and Vision with Expert Regulation for Cancer Survival Prediction: This paper proposes GROVER, a spatial multi-omics framework that captures nonlinear spatial-feature dependencies via a KAN-GCN encoder, aligns heterogeneous modalities through spot-feature-pair contrastive learning, and dynamically routes and filters low-quality signals via a self-adaptive Mixture of Experts (MoE). GROVER achieves superior clustering performance over existing methods on four real-world spatial omics datasets.
GuideGen: A Text-Guided Framework for Paired Full-Torso Anatomy and CT Volume Generation: GuideGen proposes a controllable framework that requires only text input. It synthesizes full-torso anatomical masks via a categorical diffusion model, and combines an anatomy-aware high-dynamic-range autoencoder with a latent feature generator to produce paired full-torso CT volumes, providing high-quality synthetic training data for downstream segmentation tasks.
Human-in-the-Loop Interactive Report Generation for Chronic Disease Adherence: This paper presents a "physician-in-the-loop" interactive interface that restricts AI to the roles of data organization and draft generation. Through a single-page editor, chart–text pairing, and automated urgency stratification, it enables efficient and accountable chronic disease adherence report generation. A pilot study reveals an "accountability paradox": even when AI-generated quality matches the physician manual-authoring baseline, review time cannot be significantly reduced, because clinical responsibility demands complete verification.
Intervention Efficiency and Perturbation Validation Framework: Capacity-Aware and Robust Clinical Model Selection under the Rashomon Effect: To address the challenge of model selection under the Rashomon Effect—where multiple models achieve similar performance on small, class-imbalanced clinical datasets—this paper proposes Intervention Efficiency (IE), a capacity-aware evaluation metric, and the Perturbation Validation Framework (PVF), a robustness validation framework, jointly enabling reliable model selection under resource constraints.
Learning with Preserving for Continual Multitask Learning: This paper proposes the Learning with Preserving (LwP) framework, which maintains the geometric structure of the shared representation space via a Dynamically Weighted Distance Preserving (DWDP) loss. Without requiring a replay buffer, LwP addresses catastrophic forgetting in Continual Multitask Learning (CMTL) and significantly outperforms existing continual learning methods on benchmarks including BDD100k, CelebA, and PhysiQ. It is the only method to surpass the single-task learning baseline.
MAISI-v2: Accelerated 3D High-Resolution Medical Image Synthesis with Rectified Flow and Region-specific Contrastive Loss: This paper presents MAISI-v2, the first framework to introduce Rectified Flow into 3D medical image synthesis. By replacing DDPM with Rectified Flow, it achieves a 33× speedup, and a novel region-specific contrastive loss is designed to improve conditioning fidelity for small regions such as tumors. The utility of synthesized data is validated on downstream tumor segmentation tasks.
MAPI-GNN: Multi-Activation Plane Interaction Graph Neural Network for Multimodal Medical Diagnosis: This paper proposes MAPI-GNN, which dynamically constructs multiple activation planes in semantic subspaces via a multi-dimensional feature discriminator, then aggregates intra- and inter-sample relationships through a hierarchical fusion network. The method achieves significant improvements over existing SOTA on two multimodal diagnostic tasks—prostate cancer and coronary heart disease (ACC 0.9432, AUC 0.9838 on PI-CAI).
MCTSr-Zero: Self-Reflective Psychological Counseling Dialogues Generation via Principles and Adaptive Exploration: This paper proposes the MCTSr-Zero framework, which combines MCTS with domain-principle-based self-evaluation and a meta-prompt adaptive exploration mechanism to generate high-quality multi-turn psychological counseling dialogue data. The resulting PsyLLM, fine-tuned on this data, achieves state-of-the-art performance on the authors' PsyEval benchmark.
MedEyes: Learning Dynamic Visual Focus for Medical Progressive Diagnosis: MedEyes is a hybrid-policy reinforcement learning framework that introduces a Gaze-guided Reasoning Navigator (GRN) to simulate the "scan-and-drill" visual search pattern of clinical physicians. Combined with a Confidence Value Sampler (CVS) and dual-stream GRPO optimization, the framework enables dynamic visual focus for progressive medical diagnostic reasoning, achieving an average improvement of 8.5 pp across five medical VQA benchmarks.
MindCross: Fast New Subject Adaptation with Limited Data for Cross-subject Video Reconstruction from Brain Signals: This paper proposes MindCross, a cross-subject brain decoding framework that learns subject-independent information via a shared encoder and subject-specific information via \(N\) individual encoders. Combined with a fast calibration stage and a Top-K collaborative decoding module, a single unified model achieves performance comparable to per-subject models on fMRI/EEG-to-video benchmarks, with new subject adaptation requiring only minimal data and time (~1s vs. 5–17s for baselines).
MIRNet: Integrating Constrained Graph-Based Reasoning with Pre-training for Diagnostic Medical Imaging: MIRNet is a framework that integrates self-supervised masked autoencoder (MAE) pre-training with constraint-aware graph attention network (GAT) reasoning for multi-label tongue diagnosis. The paper also introduces TongueAtlas-4K, a benchmark dataset of 4,000 images with 22 labels, achieving a 77.8% improvement in Macro Recall and 33.2% in Macro-F1.
MPA: Multimodal Prototype Augmentation for Few-Shot Learning: This paper proposes MPA, a framework that enhances prototype quality through three components: LLM-based Multi-Variant Semantic Enhancement (LMSE) for enriching semantic information, Hierarchical Multi-View Augmentation (HMA) for diversifying visual features, and an Adaptive Uncertain Class Absorber (AUCA) for modeling inter-class uncertainty. MPA achieves significant improvements over existing methods on 4 single-domain and 6 cross-domain few-shot learning benchmarks, surpassing the second-best method by 12.29% and 24.56% under the 5-way 1-shot setting for single-domain and cross-domain scenarios, respectively.
Multivariate Gaussian Representation Learning for Medical Action Evaluation: This paper proposes GaussMedAct, a framework that models joint motion trajectories as multivariate Gaussian mixture distributions combined with a Cartesian-vector dual-stream encoding scheme. It achieves 92.1% Top-1 accuracy on the newly constructed CPREval-6k dataset while requiring only 10% of the computational cost of ST-GCN.
Neural Bandit Based Optimal LLM Selection for a Pipeline of Tasks: This paper proposes Sequential Bandits, an online learning method based on neural contextual multi-armed bandits, for selecting the optimal LLM for each subtask in a task pipeline (e.g., "summarization → diagnosis"). The method jointly optimizes accuracy and cost, and outperforms existing bandit baselines on two pipeline task benchmarks: medical diagnosis prediction and telecommunications QA.
NeuroBridge: Bio-Inspired Self-Supervised EEG-to-Image Decoding via Cognitive Priors and Bidirectional Semantic Alignment: This paper proposes NeuroBridge, a framework that employs Cognitive Prior Augmentation (CPA, asymmetric augmentation to simulate perceptual variability) and a Shared Semantic Projector (SSP, bidirectional alignment into a unified semantic space). On the THINGS-EEG dataset under a 200-class zero-shot EEG-to-image retrieval task, the method achieves 63.2% Top-1 (+12.3%) and 89.9% Top-5 (+10.2%), substantially surpassing the existing state of the art.
Note2Chat: Improving LLMs for Multi-Turn Clinical History Taking Using Medical Notes: This paper proposes Note2Chat, a framework that trains LLMs for structured history taking and diagnosis using widely available medical notes rather than scarce dialogue data. Through note-driven dialogue generation, a three-stage fine-tuning strategy, and a single-turn reasoning paradigm, it substantially outperforms GPT-4o in information gathering (F1 +16.9) and diagnostic accuracy (Top-1 +21.0).
NutriScreener: Retrieval-Augmented Multi-Pose Graph Attention Network for Malnourishment Screening: This paper proposes NutriScreener, a framework combining a CLIP visual encoder, a multi-pose graph attention network (GAT), and a FAISS-based retrieval-augmented classification/regression module. Through cross-pose attention and category-enhanced retrieval, the system achieves robust childhood malnutrition detection and anthropometric prediction, attaining 0.79 recall and 0.82 AUC on cross-continental datasets including AnthroVision, with clinician ratings of 4.3/5 for accuracy and 4.6/5 for efficiency.
PanFoMa: A Lightweight Foundation Model and Benchmark for Pan-Cancer Pathology Image Analysis: This paper proposes PanFoMa, a lightweight hybrid neural network that integrates Transformer-based local modeling with Mamba-based global integration for pan-cancer single-cell transcriptomic representation learning. It also introduces PanFoMaBench, a large-scale benchmark dataset covering 33 cancer subtypes and over 3.5 million cells.
Personality-guided Public-Private Domain Disentangled Hypergraph-Former Network for Multimodal Depression Detection: This paper proposes P3HF, a framework that achieves approximately 10% gains in accuracy and F1 on multi-event multimodal depression detection through three innovations: personality-guided feature gating, a temporally-aware Hypergraph-Former architecture, and event-level public-private domain disentanglement.
Personalization of Large Foundation Models for Health Interventions: This paper systematically analyzes four structural tensions in applying large foundation models (LFMs) to personalized health interventions, argues that LFMs cannot replace N-of-1 trials, and proposes a hybrid framework that combines LFM-based hypothesis generation with causal validation via N-of-1 trials.
PINGS-X: Physics-Informed Normalized Gaussian Splatting with Axes Alignment for Efficient Super-Resolution of 4D Flow MRI: This paper proposes PINGS-X, a framework that transfers the explicit representation paradigm of 3D Gaussian Splatting (3DGS) into the domain of physics-informed super-resolution. Through three key innovations—Normalized Gaussian Splatting (NGS), axes-aligned Gaussians, and a Gaussian merging strategy—PINGS-X achieves training speeds an order of magnitude faster than PINNs while maintaining superior super-resolution accuracy on both synthetic CFD and real 4D Flow MRI datasets.
PriorRG: Prior-Guided Contrastive Pre-training and Coarse-to-Fine Decoding for Chest X-ray Report Generation: PriorRG proposes a two-stage chest X-ray report generation framework that aligns clinical context with spatiotemporal visual features via prior-guided contrastive pre-training, then progressively integrates clinical context, disease progression, and multi-level visual cues through prior-aware coarse-to-fine decoding, achieving a 3.6% improvement in BLEU-4 and a 3.8% improvement in F1 on MIMIC-CXR.
ProPL: Universal Semi-Supervised Ultrasound Image Segmentation via Prompt-Guided Pseudo-Labeling: ProPL is proposed as a framework that, for the first time, achieves universal semi-supervised ultrasound image segmentation via a shared visual encoder, prompt-guided dual decoders, and uncertainty-driven pseudo-label calibration. With only 1/16 labeled data across 5 organs and 8 tasks, it surpasses fully supervised methods by 5.18% mDice.
Provably Minimum-Length Conformal Prediction Sets for Ordinal Classification: This paper proposes min-CPS and its regularized variant min-RCPS, a model-agnostic conformal prediction method for ordinal classification. By solving the minimum-length prediction interval for each sample via a linear-time sliding window algorithm, the method reduces average prediction set size by 15% while maintaining coverage guarantees, with theoretical optimality guarantees at the instance level.
PulseMind: A Multi-Modal Medical Model for Real-World Clinical Diagnosis: This paper proposes PulseMind, a multimodal medical diagnostic model comprising three core contributions: MediScope, a large-scale multi-turn diagnostic dialogue dataset; PulseMind Benchmark, a multi-dimensional clinical dialogue evaluation benchmark; and CRPO, a comparison-based reinforcement policy optimization method. The system achieves superior performance in real-world clinical diagnostic dialogue scenarios.
Q-FSRU: Quantum-Augmented Frequency-Spectral Fusion for Medical Visual Question Answering: This paper proposes Q-FSRU, a model that transforms medical image and text features into the frequency domain via FFT for multimodal fusion, and incorporates external medical knowledge through a quantum-inspired retrieval-augmented generation (Quantum RAG) mechanism, achieving 90% accuracy and a ROC-AUC of 0.9541 on the VQA-RAD dataset.
qa-FLoRA: Data-free Query-Adaptive Fusion of LoRAs for LLMs: This paper proposes qa-FLoRA, a query-adaptive LoRA fusion method that requires neither training data nor a training process. It dynamically determines fusion weights by computing per-layer KL divergence between each adapter and the base model, achieving significant improvements over static fusion and training-free baselines across nine multilingual composite tasks.
Radiation-Preserving Selective Imaging for Pediatric Hip Dysplasia: A Cross-Modal Approach: This paper proposes an "ultrasound-first, radiation-preserving" cross-modal selective imaging strategy. By combining a self-supervised pretrained frozen encoder, a measurement-faithful lightweight head network, and a conformal-prediction-calibrated one-sided lower bound, the framework provides principled decisions on when ultrasound alone suffices and when additional X-ray imaging is warranted for diagnosing developmental dysplasia of the hip (DDH).
ReCoN-Ipsundrum: An Inspectable Recurrent Persistence Loop Agent with Affect-Coupled Cognition: This paper implements ReCoN-Ipsundrum — an inspectable agent architecture that extends the ReCoN sensorimotor state machine with Humphrey's ipsundrum recurrent persistence loop and an optional affective proxy layer. Through behavioral tests and causal ablation experiments, the paper demonstrates that recurrence supports post-stimulus persistence, affect coupling supports preference stability, structured scanning, and sustained caution, while emphasizing that behavioral markers alone are insufficient to attribute consciousness.
Refine and Align: Confidence Calibration through Multi-Agent Interaction in VQA: This paper proposes AlignVQA, a multi-agent debate framework for VQA confidence calibration: specialist agents generate candidate answers, followed by structured debate (supporting vs. opposing arguments) by generalist agents to refine confidence scores. A differentiable calibration-aware loss, AlignCal, is also introduced to minimize the upper bound of calibration error (UBCE) during training. The approach reduces ECE from 0.375 to 0.098 on VQARad and ScienceQA.
Rethinking Bias in Generative Data Augmentation for Medical AI: a Frequency Recalibration Approach: This paper identifies high-frequency distribution discrepancies between AI-generated and real medical images as the root cause of unreliable generative data augmentation (GDA), and proposes FreRec (Frequency Recalibration), a coarse-to-fine post-processing module comprising Statistical High-frequency Replacement (SHR) and Reconstructive High-frequency Mapping (RHM) to align frequency distributions, consistently improving downstream medical image classification performance as a plug-and-play component.
Rethinking Surgical Smoke: A Smoke-Type-Aware Laparoscopic Video Desmoking Method and Dataset: This paper is the first to categorize surgical smoke into two distinct types — Diffusion Smoke and Ambient Smoke — and proposes STANet, the first smoke-type-aware laparoscopic video desmoking network comprising three sub-networks: semantic soft segmentation, coarse-to-fine disentanglement, and dual-branch reconstruction. It also introduces STSVD, the first large-scale synthetic video desmoking dataset with smoke-type annotations.
Self-supervised Multiplex Consensus Mamba for General Image Fusion: This paper proposes the SMC-Mamba framework, which achieves general image fusion across infrared-visible, medical, multi-focus, and multi-exposure tasks through Modality-Agnostic Feature Enhancement (MAFE), Multiplex Consensus Cross-modal Mamba (MCCM), and Bi-level Self-supervised Contrastive Learning loss (BSCL), comprehensively surpassing state-of-the-art methods.
SEMC: Structure-Enhanced Mixture-of-Experts Contrastive Learning for Ultrasound Standard Plane Recognition: This paper proposes the SEMC framework, which aligns shallow structural cues with deep semantic representations via a Semantic-Structure Fusion Module (SSFM), and performs hierarchical contrastive learning over multi-level features through a Mixture-of-Experts Contrastive Recognition Module (MCRM), thereby enhancing fine-grained discriminability for ultrasound standard plane recognition. A new liver ultrasound dataset, LP2025, is also introduced.
Shrinking the Teacher: An Adaptive Teaching Paradigm for Asymmetric EEG-Vision Alignment: This paper proposes an Adaptive Teaching Paradigm (ATS) in which a residual-free bottleneck module, ShrinkAdapter, enables the visual "teacher" to actively shrink and restructure its knowledge to match the learning capacity of the EEG "student," achieving 60.2% Top-1 accuracy on zero-shot brain-image retrieval and surpassing the previous SOTA by 9.8 percentage points.
Sim4Seg: Boosting Multimodal Multi-disease Medical Diagnosis Segmentation with Region-Aware Vision-Language Similarity Masks: This paper introduces the Medical Diagnosis Segmentation (MDS) task along with the M3DS dataset, and proposes the Sim4Seg framework, which leverages Region-aware Vision-Language Similarity Masks (RVLS2M) derived from LVLM hidden states to prompt SAM for segmentation while simultaneously generating diagnostic chain-of-thought reasoning. Combined with a test-time scaling strategy, Sim4Seg comprehensively outperforms baselines on both segmentation and diagnosis.
Small but Mighty: Dynamic Wavelet Expert-Guided Fine-Tuning of Large-Scale Models for Optical Remote Sensing Object Segmentation: WEFT proposes a lightweight fine-tuning paradigm guided by dynamic wavelet experts, adapting frozen large-scale visual foundation models to optical remote sensing image segmentation with only 4.52% trainable parameters, surpassing 21 state-of-the-art methods on three ORSIs datasets.
SPA: Achieving Consensus in LLM Alignment via Self-Priority Optimization: This paper proposes Self-Priority Alignment (SPA), a fully unsupervised framework that enforces a strict "trustworthiness before helpfulness" priority ordering via lexicographic optimization. The model self-generates diverse responses, self-evaluates, and self-improves; dual-criterion denoising constructs preference pairs; and an uncertainty-weighted SimPO loss fine-tunes the model, simultaneously improving safety and helpfulness across multiple benchmarks.
TAlignDiff: Automatic Tooth Alignment assisted by Diffusion-based Transformation Learning: This paper proposes TAlignDiff, a unified framework that integrates a geometry-constrained point cloud regression network (PRN) with a diffusion-based transformation matrix denoising module (DTMD) under a joint training paradigm. Through a bidirectional feedback mechanism, the framework achieves superior automatic tooth alignment on small-scale clinical datasets compared to existing methods.
Towards Effective and Efficient Context-aware Nucleus Detection in Histopathology Whole Slide Images: This paper proposes an efficient context-aware nucleus detection method that aggregates off-the-shelf features from historically visited sliding windows—rather than additionally cropping large low-field-of-view patches—to provide tissue context, while employing a cross-annotation strategy to mine surrounding unannotated nucleus samples for enhanced contextual adaptability.
Training-Free Policy Violation Detection via Activation-Space Whitening in LLMs: This work reformulates LLM policy violation detection as an out-of-distribution (OOD) detection problem in activation space. A training-free whitening approach is proposed: a whitening transform is fitted on compliant activations, and the Euclidean norm serves as the compliance score. Deployment requires only policy text and a small number of examples. The method achieves 86.0% F1 on DynaBench, outperforming fine-tuned baselines by 9.1 points and LLM-as-Judge by 16 points.
Unsupervised Motion-Compensated Decomposition for Cardiac MRI Reconstruction via Neural Representation: This paper proposes MoCo-INR, which for the first time integrates implicit neural representation (INR) into a motion compensation (MoCo) framework. Through an unsupervised approach, it achieves high-quality dynamic reconstruction of cardiac MRI, significantly outperforming existing unsupervised methods at ultra-high acceleration factors (20× Cartesian / 69× Non-Cartesian).
Unsupervised Multi-Parameter Inverse Solving for Reducing Ring Artifacts in 3D X-Ray CBCT: This paper proposes Riner, which formulates CT ring artifact removal (RAR) as a physics-based multi-parameter inverse problem. By jointly learning artifact-free images and detector physical parameters via implicit neural representation (INR), Riner achieves unsupervised 3D CBCT reconstruction that surpasses supervised state-of-the-art methods.
Vascular Anatomy-aware Self-supervised Pre-training for X-ray Angiogram Analysis: This paper proposes VasoMIM, a domain-specific self-supervised pre-training framework for X-ray angiograms. It introduces an anatomy-guided masking strategy that prioritizes vessel regions, an anatomical consistency loss to preserve vascular topology in reconstructed images, and a newly constructed XA-170K pre-training dataset — the largest of its kind. VasoMIM comprehensively outperforms both general-purpose and medical SSL methods (including DINOv3 pre-trained on 1.69 billion images) across 4 downstream tasks and 6 datasets.
VitalDiagnosis: AI-Driven Ecosystem for 24/7 Vital Monitoring and Chronic Disease Management: This paper proposes VitalDiagnosis, an LLM-driven chronic disease management ecosystem that integrates continuous wearable data with multi-scale LLM reasoning, establishing a dual-track framework comprising interactive anomaly triage and routine adherence monitoring, thereby enabling a paradigm shift from passive surveillance to active engagement within a collaborative patient–clinician workflow.
WDT-MD: Wavelet Diffusion Transformers for Microaneurysm Detection in Fundus Images: This paper proposes WDT-MD, a framework that addresses three fundamental challenges in fundus image microaneurysm (MA) detection—identity mapping, high false positives, and poor normal-feature reconstruction quality—through noise-encoded image conditioning, pseudo-normal pattern synthesis, and a wavelet diffusion Transformer architecture.