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Dongyue Chen

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8 papers

1 author row

EAAI Journal 2026 Journal Article

Integrating whole-slide images and transcriptomic data for survival analysis using multimodal attention networks

Chunfeng Shao
Yuanshen Zhao
Yinsheng Chen
Jingxian Duan
Zeyu Zhang
Rongpin Wang
Dong Liang
Dongyue Chen

The integration of multimodal data holds great promise for tumor prognosis prediction by providing a more comprehensive view of the tumor. However, current fusion algorithms, which either merge feature levels or use alignment mechanisms for complex integration, often overlook the relationships between different modalities. To address this issue, we propose a novel multimodal deep learning algorithm that integrates pathological images and transcriptomic data for tumor prognosis prediction. Our model not only uses information from each data modality but also mines the correlations between the two modalities through a Hierarchical Cross-Modal Attention (HCMA) mechanism. In the pathological branch, we designed a self-attention encoder and decoder to extract various levels of contextual features from the pathological images. In the transcriptomic branch, we employed a selective state space module specifically designed to capture the complex dependencies among genetic features. To capture the correlations between the two modalities, we used an HCMA transformer module to generate cross-modal features, including gene-guided pathological features and pathology-guided gene features. Finally, we apply a specific feature alignment mechanism to constrain these features before fusing them to predict survival outcomes. We validated the proposed algorithm on four diverse cancer datasets from The Cancer Genome Atlas (TCGA). For testing the performance of the proposed survival prediction model, we also validated it on an independent cohort of 86 glioma patients from Sun Yat-sen University Cancer Center. Results show the model performs excellently in cancer survival analysis, which outperforms comparative single-modal models and multimodal fusion models.

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NeurIPS Conference 2025 Conference Paper

CSPCL: Category Semantic Prior Contrastive Learning for Deformable DETR-Based Prohibited Item Detectors

Mingyuan Li
Tong Jia
Hao Wang
Bowen Ma
Shiyi Guo
Da Cai
Dongyue Chen

Prohibited item detection based on X-ray images is one of the most effective security inspection methods. However, the foreground-background feature coupling caused by the overlapping phenomenon specific to X-ray images makes general detectors designed for natural images perform poorly. To address this issue, we propose a Category Semantic Prior Contrastive Learning (CSPCL) mechanism, which aligns the class prototypes perceived by the classifier with the content queries to correct and supplement the missing semantic information responsible for classification, thereby enhancing the model sensitivity to foreground features. To achieve this alignment, we design a specific contrastive loss, CSP loss, which comprises the Intra-Class Truncated Attraction (ITA) loss and the Inter-Class Adaptive Repulsion (IAR) loss, and outperforms classic contrastive losses. Specifically, the ITA loss leverages class prototypes to attract intra-class content queries and preserves essential intra-class diversity via a gradient truncation function. The IAR loss employs class prototypes to adaptively repel inter-class content queries, with the repulsion strength scaled by prototype-prototype similarity, thereby improving inter-class discriminability, especially among similar categories. CSPCL is general and can be easily integrated into Deformable DETR-based models. Extensive experiments on the PIXray, OPIXray, PIDray, and CLCXray datasets demonstrate that CSPCL significantly enhances the performance of various state-of-the-art models without increasing inference complexity. The code is publicly available at https: //github. com/Limingyuan001/CSPCL.

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EAAI Journal 2025 Journal Article

Detection of novel prohibited item categories for real-world security inspection

Shuyang Lin
Tong Jia
Hao Wang
Bowen Ma
Mingyuan Li
Dongyue Chen

The application of prohibited item detection in intelligent security inspection systems is essential for protecting public safety and categories of prohibited item are continuously increasing in accordance with the latest laws. Previous works all focus on closed-set scenarios, which can only recognize known categories used for training and often require time-consuming as well as labor-intensive annotations when learning novel categories, resulting in limited real-world applications. Although the success of vision-language models such us Contrastive Language-Image Pretraining (CLIP) provides a new perspective for X-ray prohibited item detection in open scenarios, directly applying CLIP to X-ray domain leads to a sharp performance drop due to domain shift between X-ray data and general data used for pre-training CLIP. To address aforementioned challenges, in this paper, we introduce distillation-based open-vocabulary object detection (OVOD) task into X-ray security inspection domain by extending CLIP to learn visual representations in specific X-ray domain, aiming to detect novel prohibited item categories beyond base categories on which the detector is trained. Specifically, we propose X-ray feature adapter and apply it to CLIP within OVOD framework to develop Open-Vocabulary X-ray prohibited item Detection (OVXD) model. Extensive experiments conducted on three mainstream X-ray security inspection datasets demonstrate that OVXD performs favorably against baseline methods in X-ray scenarios. Furthermore, OVXD can be directly transferred to different X-ray datasets without fine-tuning, verifying its generalization ability in real-world applications.

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AAAI Conference 2025 Conference Paper

Efficient Indoor Depth Completion Network Using Mask-adaptive Gated Convolution

Tingxuan Huang
Jiacheng Miao
Shizhuo Deng
Tong Jia
Dongyue Chen

Most indoor depth completion tasks rely on convolutional auto-encoders to reconstruct depth images, especially in areas with significant missing values. While traditional convolution treats valid and missing pixels equally, Partial Convolution (PConv) has mitigated this limitation. However, PConv fails to distinguish the varying degree of invalidity across different missing areas, which highlights the need for a more refined strategy. To solve this problem, we propose a novel system for indoor depth completion tasks that leverages Mask-adaptive Gated Convolution (MagaConv). MagaConv utilizes gated signals to selectively apply convolution kernels based on the characteristics of missing depth data. These gating signals are generated using shared convolution kernels that jointly process depth features and corresponding masks, ensuring coherent weight optimization. Additionally, the mask undergoes iterative updates according to predefined rules. To improve the fusion of depth and color information, we introduce a Bi-directional Aligning Projection (Bid-AP) module, which utilizes a bi-directional projection scheme with global spatial-channel attention mechanisms to filter out depth-irrelevant features from other modalities. Extensive experiments on popular benchmarks, including NYU-Depth V2, DIML, and SUN RGB-D, demonstrate that our model outperforms state-of-the-art methods in both accuracy and efficiency.

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AAAI Conference 2025 Conference Paper

Hierarchical Classification Auxiliary Network for Time Series Forecasting

Yanru Sun
Zongxia Xie
Dongyue Chen
Emadeldeen Eldele
Qinghua Hu

Deep learning has significantly advanced time series forecasting through its powerful capacity to capture sequence relationships. However, training these models with the Mean Square Error (MSE) loss often results in over-smooth predictions, making it challenging to handle the complexity and learn high-entropy features from time series data with high variability and unpredictability. In this work, we introduce a novel approach by tokenizing time series values to train forecasting models via cross-entropy loss, while considering the continuous nature of time series data. Specifically, we propose a Hierarchical Classification Auxiliary Network, HCAN, a general model-agnostic component that can be integrated with any forecasting model. HCAN is based on a Hierarchy-Aware Attention module that integrates multi-granularity high-entropy features at different hierarchy levels. At each level, we assign a class label for timesteps to train an Uncertainty-Aware Classifier. This classifier mitigates the over-confidence in softmax loss via evidence theory. We also implement a Hierarchical Consistency Loss to maintain prediction consistency across hierarchy levels. Extensive experiments integrating HCAN with state-of-the-art forecasting models demonstrate substantial improvements over baselines on several real-world datasets.

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NeurIPS Conference 2025 Conference Paper

Learning Pattern-Specific Experts for Time Series Forecasting Under Patch-level Distribution Shift

Yanru Sun
Zongxia Xie
Emadeldeen Eldele
Dongyue Chen
Qinghua Hu
Min Wu

Time series forecasting, which aims to predict future values based on historical data, has garnered significant attention due to its broad range of applications. However, real-world time series often exhibit heterogeneous pattern evolution across segments, such as seasonal variations, regime changes, or contextual shifts, making accurate forecasting challenging. Existing approaches, which typically train a single model to capture all these diverse patterns, often struggle with the pattern drifts between patches and may lead to poor generalization. To address these challenges, we propose TFPS, a novel architecture that leverages pattern-specific experts for more accurate and adaptable time series forecasting. TFPS employs a dual-domain encoder to capture both time-domain and frequency-domain features, enabling a more comprehensive understanding of temporal dynamics. It then performs subspace clustering to dynamically identify distinct patterns across data segments. Finally, these patterns are modeled by specialized experts, allowing the model to learn multiple predictive functions. Extensive experiments on real-world datasets demonstrate that TFPS outperforms state-of-the-art methods, particularly on datasets exhibiting significant distribution shifts. The data and code are available: https: //github. com/syrGitHub/TFPS.

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JBHI Journal 2024 Journal Article

LHAR: Lightweight Human Activity Recognition on Knowledge Distillation

Shizhuo Deng
Jiaqi Chen
Da Teng
Chuangui Yang
Dongyue Chen
Tong Jia
Hao Wang

Sensor-based Human Activity Recognition (HAR) is widely used in daily life and is the basic-level bridge to virtual healthcare in the metaverse. The current challenge is the low recognition accuracy for personalized users on smart wearable devices. The limited resource cannot support large deep learning models updated locally. Besides, integrating and transmitting sensor data to the cloud would reduce the efficiency. Considering the tradeoff between performance and complexity, we propose a Lightweight Human Activity Recognition (LHAR) framework. In LHAR, we combine the cross-people HAR task with the lightweight model task. LHAR framework is designed on the teacher-student architecture and the student network consists of multiple depthwise separable convolution layers to achieve fewer parameters. The dark knowledge distilled from the complex teacher model enhances the generalization ability of LHAR. To achieve effective knowledge distillation, we propose two optimization methods. Firstly, we train the teacher model by ensemble learning to promote teacher performance. Secondly, a multi-channel data augmentation method is proposed for the diversity of the dataset, which is a plug-in operation for the ensemble teacher model. In the experiments, we compare LHAR with state-of-art models in comparison evaluation, ablation study and the hyperparameter analysis, which proves the better performance of LHAR in efficiency and effectiveness.

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NeurIPS Conference 2024 Conference Paper

Persistence Homology Distillation for Semi-supervised Continual Learning

Yan Fan
Yu Wang
Pengfei Zhu
Dongyue Chen
Qinghua Hu

Semi-supervised continual learning (SSCL) has attracted significant attention for addressing catastrophic forgetting in semi-supervised data. Knowledge distillation, which leverages data representation and pair-wise similarity, has shown significant potential in preserving information in SSCL. However, traditional distillation strategies often fail in unlabeled data with inaccurate or noisy information, limiting their efficiency in feature spaces undergoing substantial changes during continual learning. To address these limitations, we propose Persistence Homology Distillation (PsHD) to preserve intrinsic structural information that is insensitive to noise in semi-supervised continual learning. First, we capture the structural features using persistence homology by homological evolution across different scales in vision data, where the multi-scale characteristic established its stability under noise interference. Next, we propose a persistence homology distillation loss in SSCL and design an acceleration algorithm to reduce the computational cost of persistence homology in our module. Furthermore, we demonstrate the superior stability of PsHD compared to sample representation and pair-wise similarity distillation methods theoretically and experimentally. Finally, experimental results on three widely used datasets validate that the new PsHD outperforms state-of-the-art with 3. 9% improvements on average, and also achieves 1. 5% improvements while reducing 60% memory buffer size, highlighting the potential of utilizing unlabeled data in SSCL. Our code is available: https: //github. com/fanyan0411/PsHD.

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