Author name cluster

Zhao Huang

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5 papers

2 author rows

AIIM Journal 2026 Journal Article

CA-OCL and CHAN: A novel diagnostic framework for rheumatoid arthritis integrating contradiction-aware orthogonal contrastive learning with confidence-guided hierarchical attention

Zhao Huang
QingMei Zeng
NanNan Gai

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disorder characterized by progressive destruction of synovial joints, for which precise early diagnosis is critical to effective clinical management. Although current computer-aided diagnosis (CAD) systems show promising potential, their practical deployment remains challenged by heterogeneity in multimodal data and the inherent complexity of pathophysiological manifestations. A key limitation of existing approaches is the absence of dedicated mechanisms to reconcile inter-modal conflicts and to adequately leverage the diagnostic information present in contradictory samples, such as discordances between laboratory findings and clinical descriptions, which often leads to suboptimal diagnostic performance. To overcome these challenges, this study introduces a novel auxiliary diagnostic framework for RA based on Contradiction-Aware Orthogonal Contrastive Learning (CA-OCL) and a Confidence-guided Hierarchical Attention Network (CHAN). The proposed architecture incorporates three major innovations. First, in the data processing stage, a lightweight feature-crossing network is employed to derive robust representations from structured data, while a multi-task adapted extension of the BERT model is utilized to extract rich semantic features from unstructured textual inputs. Second, the CA-OCL module is designed to explicitly identify and learn from contradictory negative samples—a capability largely absent in conventional contrastive learning frameworks. Additionally, orthogonal constraints are applied to minimize feature redundancy across modalities, thereby preserving discriminative modality-specific information that is often obscured by methods promoting excessive feature alignment. Finally, the CHAN module dynamically modulates inter-modal contributions using confidence estimates, mitigating the risk of unilateral dominance by any single modality, a common drawback in attention-based fusion mechanisms and facilitating refined integration of conflicting information. Comprehensive experimental evaluations demonstrate that the proposed framework achieves superior performance compared to state-of-the-art methods. These results not only validate the efficacy of our approach in handling multimodal conflicts and exploiting contradictory evidence, but also highlight its significant clinical utility through effective multimodal data integration. This work addresses critical limitations in conventional CAD systems and provides an advanced paradigm for intelligent diagnostic assessment of RA.

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EAAI Journal 2026 Journal Article

The importance of data noise reduction–wavelet transformation in stock price forecasting with Bidirectional Long Short-Term Memory network

Honglei Li
Yukesh Marudhasalam
Zhao Huang
Jiuhong Yu

With the rapid development of implemented artificial intelligence, various deep learning models are proposed. Unlike conventional models, Bi-directional Long-Short-Term Memory (BiLSTM)’s architecture allows for the capture of both forward and backward dependencies in data, making it particularly effective in detecting trends and seasonality. This study explores the application of BiLSTM networks for stock price forecasting, focusing on multivariate time-series data. In this work, a novel approach integrates Wavelet transformation with BiLSTM to decompose stock price data into trend and seasonal components, improving model interpretability and forecast accuracy. The model is further enhanced by incorporating daily stock price deviations as an additional feature during training. The effectiveness of this approach is demonstrated by comparing the performance of BiLSTM with other deep learning models, including LSTM, Gated Recurrent Unit, and Recurrent Neural Networks, across two stock datasets. Experimental results show substantial gains: up to 250 × reduction in Mean Squared Error (MSE), 20 × in Mean Absolute Error (MAE), and 15 × in Mean Absolute Percentage Error (MAPE) after applying the wavelet transform; relative to LSTM, the proposed model achieves 4 × lower MSE, 2 × lower MAE, and 2. 5 × lower MAPE, validating the robustness of the proposed method for capturing both trend and seasonality in financial time-series data. The results verify the importance of applications of artificial intelligence.

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AAAI Conference 2026 Conference Paper

Wavefront-Constrained Passive Obscured Object Detection

Zhiwen Zheng
Yiwei Ouyang
Zhao Huang
Tao Zhang
Xiaoshuai Zhang
Huiyu Zhou
Wenwen Tang
Shaowei Jiang

Accurately localizing and segmenting obscured objects from faint light patterns beyond the field of view is highly challenging due to multiple scattering and medium-induced perturbations. Most existing methods, based on real-valued modeling or local convolutional operations, are inadequate for capturing the underlying physics of coherent light propagation. Moreover, under low signal-to-noise conditions, these methods often converge to non-physical solutions, severely compromising the stability and reliability of the observation. To address these challenges, we propose a novel physics-driven Wavefront Propagating Compensation Network (WavePCNet) to simulate wavefront propagation and enhance the perception of obscured objects. This WavePCNet integrates the Tri-Phase Wavefront Complex-Propagation Reprojection (TriWCP) to incorporate complex amplitude transfer operators to precisely constrain coherent propagation behavior, along with a momentum memory mechanism to effectively suppress the accumulation of perturbations. Additionally, a High-frequency Cross-layer Compensation Enhancement is introduced to construct frequency-selective pathways with multi-scale receptive fields and dynamically models structural consistency across layers, further boosting the model’s robustness and interpretability under complex environmental conditions. Extensive experiments conducted on four physically collected datasets demonstrate that WavePCNet consistently outperforms state-of-the-art methods across both accuracy and robustness.

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ICLR Conference 2025 Conference Paper

Trajectory-LLM: A Language-based Data Generator for Trajectory Prediction in Autonomous Driving

Kairui Yang
Zihao Guo
Gengjie Lin
Haotian Dong
Zhao Huang
Yipeng Wu
Die Zuo
Jibin Peng

Vehicle trajectory prediction is a crucial aspect of autonomous driving, which requires extensive trajectory data to train prediction models to understand the complex, varied, and unpredictable patterns of vehicular interactions. However, acquiring real-world data is expensive, so we advocate using Large Language Models (LLMs) to generate abundant and realistic trajectories of interacting vehicles efficiently. These models rely on textual descriptions of vehicle-to-vehicle interactions on a map to produce the trajectories. We introduce Trajectory-LLM (Traj-LLM), a new approach that takes brief descriptions of vehicular interactions as input and generates corresponding trajectories. Unlike language-based approaches that translate text directly to trajectories, Traj-LLM uses reasonable driving behaviors to align the vehicle trajectories with the text. This results in an "interaction-behavior-trajectory" translation process. We have also created a new dataset, Language-to-Trajectory (L2T), which includes 240K textual descriptions of vehicle interactions and behaviors, each paired with corresponding map topologies and vehicle trajectory segments. By leveraging the L2T dataset, Traj-LLM can adapt interactive trajectories to diverse map topologies. Furthermore, Traj-LLM generates additional data that enhances downstream prediction models, leading to consistent performance improvements across public benchmarks. The source code is released at https://github.com/TJU-IDVLab/Traj-LLM.

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IJCAI Conference 2025 Conference Paper

Volumetric Axial Disentanglement Enabling Advancing in Medical Image Segmentation

Xingru Huang
Jian Huang
Yihao Guo
Tianyun Zhang
Zhao Huang
Yaqi Wang
Ruipu Tang
Guangliang Cheng

Information retrieved from three dimensions is treated uniformly in CNN-based volumetric segmentation methods. However, such neglect of axial disparities fails to capture true spatio-temporal variations. This paper introduces the volumetric axial disentanglement to address the disparities in spatial information along different axial dimensions. Building on this concept, we propose the Post-Axial Refiner (PaR) module to refine segmentation masks by implementing axial disentanglement on the specific axis of the volumetric medical sequences. As a plug-and-play enhancement to existing volumetric segmentation architecture, PaR further utilizes specialized attention approaches to learn disentangled post-decoding features, enhancing spatial representation and structural detail. Validation on various datasets demonstrates PaR's consistent elevation of segmentation precision and boundary clarity across 11 baselines and different imaging modalities, achieving state-of-the-art performance on multiple datasets. Experimental tests demonstrate the ability of volumetric axial disentanglement to refine the segmentation of volumetric medical images. Code is released at https: //github. com/IMOP-lab/PaR-Pytorch.

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