6 citations · 21 across the 38 of their papers we have counts for
38 papers
PGP-Clinical-TimeKAN: Prior-Guided Joint Probabilistic Forecasting of Clinical Trajectories
Weizhi Nie, Rihao Chang, Weijie Wang +1
Clinical deterioration unfolds through coupled, partially observed trajectories, not a single diagnostic label. We introduce PGP-Clinical-TimeKAN, a trajectory-first framework for…
Patient-Conditioned Dual Hypergraph Reasoning for Auditable Traditional Chinese Medicine Prescription Support
Weizhi Nie, Shaojin Bai, Weijie Wang +1
Traditional Chinese medicine (TCM) prescription support requires patient-specific reasoning from clinical narratives to syndromes, treatment principles, herbs, and doses. Direct la…
Liquid Latent State Dynamics for Interpretable Turbofan Degradation Modeling
Weizhi Nie, Weijie Wang, Yuting Su
Multivariate time-series models for prognostics are often evaluated by point prediction accuracy, yet their internal states rarely expose a coherent degradation process. We study l…
Prototype Latent World Model Replay for Class-Incremental Learning
Weizhi Nie, Hui Wang, Weijie Wang +1
Class-incremental learning requires a model to learn new classes while preserving decision regions for old ones. This is difficult when raw old samples are no longer available. We…
Hypergraph Normal World Models for Logical Visual Anomaly Detection
Weizhi Nie, Zibo Xu, Weijie Wang +1
Visual anomaly detection is often deployed with only normal training images. Most one-class detectors map test patches or features to a normal reference distribution. This works we…
Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection
Weizhi Nie, Weichao Liu, Weijie Wang +1
Abnormality detection in complex systems faces two practical barriers: abnormal labels are scarce, and binary labels do not quantify how far an event has departed from normal behav…
Phys-JEPA: Physics-Informed Latent World Models for Multivariate Time-Series Forecasting
Weizhi Nie, Weichao Liu, Honglin Guo +1
Multivariate forecasting in physical systems requires models that predict coupled temporal variables while preserving meaningful state evolution. Deep forecasters can fit temporal…
Ask4VG: Risk-Aware Question Selection for Reducing Prior-Driven Answers in Medical VQA
Xiaorong Zhu, Qiang Li, Zibo Xu +2
Medical visual question answering requires models to ground their responses in image evidence, because visually unsupported answers can mislead downstream interpretation. However,…
NTIRE 2026 3D Restoration and Reconstruction in Real-world Adverse Conditions: RealX3D Challenge Results
Shuhong Liu, Chenyu Bao, Ziteng Cui +103
This paper presents a comprehensive review of the NTIRE 2026 3D Restoration and Reconstruction (3DRR) Challenge, detailing the proposed methods and results. The challenge seeks to…
Clinically Interpretable Sepsis Early Warning via LLM-Guided Simulation of Temporal Physiological Dynamics
Weizhi Nie, Zhen Qu, Weijie Wang +4
Timely and interpretable early warning of sepsis remains a major clinical challenge due to the complex temporal dynamics of physiological deterioration. Traditional data-driven mod…
Dual Causal Inference: Integrating Backdoor Adjustment and Instrumental Variable Learning for Medical VQA
Zibo Xu, Qiang Li, Ke Lu +3
Medical Visual Question Answering (MedVQA) aims to generate clinically reliable answers conditioned on complex medical images and questions. However, existing methods often overfit…
Causal-Transformer with Adaptive Mutation-Locking for Early Prediction of Acute Kidney Injury
Weizhi Nie, Haolin Chen
Accurate early prediction of Acute Kidney Injury (AKI) is critical for timely clinical intervention. However, existing deep learning models struggle with irregularly sampled data a…
CSRA: Controlled Spectral Residual Augmentation for Robust Sepsis Prediction
Honglin Guo, Rihao Chang, He Jiao +3
Accurate prediction of future risk and disease progression in sepsis is clinically important for early warning and timely intervention in intensive care. However, short-window seps…
Unlabeled Cross-Center Automatic Analysis for TAAD: An Integrated Framework from Segmentation to Clinical Features
Mengdi Liu, Qiang Li, Weizhi Nie +2
Type A Aortic Dissection (TAAD) is a life-threatening cardiovascular emergency that demands rapid and precise preoperative evaluation. While key anatomical and pathological feature…
CIV-DG: Conditional Instrumental Variables for Domain Generalization in Medical Imaging
Shaojin Bai, Yuting Su, Weizhi Nie
Cross-site generalizability in medical AI is fundamentally compromised by selection bias, a structural mechanism where patient demographics (e.g., age, severity) non-randomly dicta…
Mask-Guided Attention Regulation for Anatomically Consistent Counterfactual CXR Synthesis
Zichun Zhang, Weizhi Nie, Honglin Guo +1
Counterfactual generation for chest X-rays (CXR) aims to simulate plausible pathological changes while preserving patient-specific anatomy. However, diffusion-based editing methods…
Learning to Trim: End-to-End Causal Graph Pruning with Dynamic Anatomical Feature Banks for Medical VQA
Zibo Xu, Qiang Li, Weizhi Nie +1
Medical Visual Question Answering (MedVQA) models often exhibit limited generalization due to reliance on dataset-specific correlations, such as recurring anatomical patterns or qu…
Organ-Agents: Virtual Human Physiology Simulator via LLMs
Rihao Chang, He Jiao, Weizhi Nie +13
Recent advances in large language models (LLMs) have enabled new possibilities in simulating complex physiological systems. We introduce Organ-Agents, a multi-agent framework that…
Structure Causal Models and LLMs Integration in Medical Visual Question Answering
Zibo Xu, Qiang Li, Weizhi Nie +2
Medical Visual Question Answering (MedVQA) aims to answer medical questions according to medical images. However, the complexity of medical data leads to confounders that are diffi…
FreeInsert: Disentangled Text-Guided Object Insertion in 3D Gaussian Scene without Spatial Priors
Chenxi Li, Weijie Wang, Qiang Li +3
Text-driven object insertion in 3D scenes is an emerging task that enables intuitive scene editing through natural language. However, existing 2D editing-based methods often rely o…
Causal Disentanglement for Robust Long-tail Medical Image Generation
Weizhi Nie, Zichun Zhang, Weijie Wang +3
Counterfactual medical image generation effectively addresses data scarcity and enhances the interpretability of medical images. However, due to the complex and diverse pathologica…
TRCE: Towards Reliable Malicious Concept Erasure in Text-to-Image Diffusion Models
Ruidong Chen, Honglin Guo, Lanjun Wang +3
Recent advances in text-to-image diffusion models enable photorealistic image generation, but they also risk producing malicious content, such as NSFW images. To mitigate risk, con…
CAT-DM: Controllable Accelerated Virtual Try-on with Diffusion Model
Jianhao Zeng, Dan Song, Weizhi Nie +3
Generative Adversarial Networks (GANs) dominate the research field in image-based virtual try-on, but have not resolved problems such as unnatural deformation of garments and the b…
MV-CLIP: Multi-View CLIP for Zero-shot 3D Shape Recognition
Dan Song, Xinwei Fu, Ning Liu +5
Large-scale pre-trained models have demonstrated impressive performance in vision and language tasks within open-world scenarios. Due to the lack of comparable pre-trained models f…