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Lei Cao

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14 papers

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YNIMG Journal 2026 Journal Article

Cerebro-cerebellar structure-function coupling’s role in motor recovery after infarction

Jing Liu
Yi Shan
Bi-Xiao Cui
Zhen-Ming Wang
Shao-Zhen Yan
Jie Xu
Lin-Lin Ye
Lei Cao

OBJECTIVE: To investigate the pathway-specific structure-function coupling induced by focal subcortical infarction and its influence on clinical symptoms. METHODS: In this prospective study, 50 patients with unilateral subcortical infarction and motor impairment and 50 matched controls underwent resting state fMRI, DTI, and Fugl-Meyer-Assessment lower-extremity (FMA-LE) at 7-14- and 30-days post-infarction. To analyze the pathway-specific structure-function coupling, we evaluated the association between structural integrity of the corticospinal tract (CST), dentate thalamocortical tract (DTCT), cortico-pontocerebellar tract (CPCT), and dorsal spinocerebellar tract (DSCT) and functional connectivity (FC) of corresponding subregions. Moderation analysis assesses whether the structure-function coupling pathway moderates FMA-LE. RESULTS: At baseline, patients exhibited significantly lower structural integrity of DTCT, DSCT, and CST than controls. We found structure-function couplings in the three motor pathways of the cerebro-cerebellar circuit: (1) contralesional thalamus to ipsilesional cerebellum-crus_2 with dentate thalamocortical tract (DTCT), (2) contralesional thalamus to cerebellum vermis_10 with dorsal spinocerebellar tract (DSCT), (3) ipsilesional precentral gyrus to frontal medial gyrus with CST. The baseline DSCT structural integrity specificity modulates the relationship between FC and FMA-LE over 30 days. CONCLUSIONS: We observed that cerebro-cerebellar circuit structure-function coupling after infarction, based on its anatomy and mapped to motor function (with DSCT as the key pathway mediating/moderating prognosis), serves as a potent biomarker for lower limb prognosis and a basis for precise rehabilitation.

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JBHI Journal 2026 Journal Article

Few-Shot Class-Incremental Learning With Dynamic Prototype Refinement for Brain Activity Classification

Lei Cao
Hao Li
Yilin Dong
Tianyu Liu
Jie Li

The brain-computer interface (BCI) system facilitates efficient communication and control, with Electroencephalography (EEG) signals as a vital component. Traditional EEG signal classification, based on static deep-learning models, presents a challenge when new classes of the subject’s brain activity emerge. The goal is to develop a model that can recognize new few-shot classes while preserving its ability to discriminate between existing ones. This scenario is referred to as Few-Shot Class-Incremental Learning (FSCIL). This work introduces IncrementEEG, a novel framework meticulously designed to tackle the distinct challenges of FSCIL in EEG-based brain activity classification, focusing specifically on emotion recognition and steady-state visual evoked potential (SSVEP). Our work analyzes the role of additive angular margin loss in improving the model’s discrimination capabilities. The proposed method is designed to demonstrate robustness in open-world conditions and adaptability to new tasks. Furthermore, we introduce a prototype refinement module comprising a prototype augmentation block and an update block. The prototype augmentation block in the deep feature space preserves the decision boundary for prior tasks, and the prototype update block utilizes a shared embedding space to compute the relation matrix for bootstrapping prototype updates. Extensive experiments conducted across multiple datasets show the superior performance of the IncrementEEG framework compared to state-of-the-art methods. The proposed method advances FSCIL brain activity classification, offering promising potential for applications in Brain-Computer Interface systems.

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AIIM Journal 2025 Journal Article

A cell-interacting and multi-correcting method for automatic circulating tumor cells detection

Xuan Zhang
Rensheng Lai
Ling Bai
Jianxin Ji
Ruihao Qin
Lihong Jiang
Bin Meng
Ying Zhang

Sensitive detection of circulating tumor cells (CTCs) from peripheral blood can serve as an effective tool in the early diagnosis and prognosis of cancer. Many methods based on modern object detectors were proposed in recent years for automatic abnormal cells detection in slide images. Although the modes of these methods can also be applied to the CTCs detection, several practical difficulties lead to suboptimal performance of them, such as accurate capture of CTCs in a large number of mixed cells and identification of CTCs and CTC-like cells with similar visual characteristics. Here, we develop a new cell-interacting and multi-correcting detector called CMD, and apply H&E-stained slide images to detect CTCs automatically for the first time. Specifically, the proposed method incorporates two task-oriented novel modules: (1) a self-attention module for aggregating feature interactions between cells and allowing the model to pay more attention to key abnormal cells, (2) a hard sample mining sampler for progressively correcting predictions of cells with ambiguous classification boundaries. Experiments conducted on a multi-center dataset of 1247 annotated slide images confirm the superiority of our method over state-of-the-art cell detection methods. The results of ablation experiment part also prove the effectiveness of two modules. The source codes of this paper are available at https: //github. com/zx333445/CMD.

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JBHI Journal 2025 Journal Article

Cross-Domain Human Activity Recognition via Domain Adaptation and Fused Attention

Tianyun Zhu
Yilin Dong
Yong Zhou
Changming Zhu
Lei Cao

In recent years, the utilization of wearable sensors for Human Activity Recognition (HAR) has garnered significant interest in the fields of medical health monitoring and sports management. However, HAR often suffer the poor generalization from the insufficient labeled data for complex activities. To address this issue, the novel Transfer Component Analysis-Bidirectional Long Short-Term Memory network (TCA-BiLSTM) with the fused attention mechanism is presented in this paper. Specifically, TCA-BiLSTM first leverages the Maximum Mean Difference (MMD) within the Reproducing Kernel Hilbert Space (RKHS) to learn transfer components for sensor-based HAR. These derived transfer components align the data collected from sensors deployed on different body parts, facilitating the mapping of cross-domain HAR data. Then, the two-layer BiLSTM with the novel fused attention mechanism is given to classify the unseen activities, which aims to capture the multi-granularity activity information after the TCA-based domain adaptation. To evaluate the effectiveness of TCA-BiLSTM, a series of experiments were conducted using the DSADS and PAMAP2 datasets. The results demonstrate that TCA-BiLSTM outperforms the state-of-art methods such as DSAN and FNet, achieving performance improvements of 6. 1% and 2. 5%, respectively.

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IJCAI Conference 2025 Conference Paper

DGraFormer: Dynamic Graph Learning Guided Multi-Scale Transformer for Multivariate Time Series Forecasting

Han Yan
Dongliang Chen
Guiyuan Jiang
Bin Wang
Lei Cao
Junyu Dong
Yanwei Yu

Multivariate time series forecasting is a critical focus across many fields. Existing transformer-based models have overlooked the explicit modeling of inter-variable correlations. Similarly, the graph-based methods have also failed to address the dynamic nature of multivariate correlations and the noise in correlation modeling. To overcome these challenges, we propose a novel Dynamic Graph Learning Guided Multi-Scale Transformer (DGraFormer) for multivariate time series forecasting. Specifically, our method consists of two main components: Dynamic correlation-aware graph Learning (DCGL) and multi-scale temporal transformer (MTT). The former aims to capture dynamic correlations across different time windows, filters out noise, and selects key weights to guide the aggregation of relevant feature representations. The latter can effectively extract temporal patterns from patch data at varying scales. Finally, the proposed method can capture rich local correlation graph structures and multi-scale global temporal features. Experimental results demonstrate that DGraformer significantly outperforms existing state-of-the-art models on ten real-world datasets, achieving the best performance across multiple evaluation metrics. The source code of our model is available at \url{https: //anonymous. 4open. science/r/DGraFormer}.

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NeurIPS Conference 2025 Conference Paper

Handling Label Noise via Instance-Level Difficulty Modeling and Dynamic Optimization

Kuan Zhang
Chengliang Chai
Jingzhe Xu
Chi Zhang
Han Han
Ye Yuan
Guoren Wang
Lei Cao

Recent studies indicate that deep neural networks degrade in generalization performance under noisy supervision. Existing methods focus on isolating clean subsets or correcting noisy labels, facing limitations such as high computational costs, heavy hyperparameter tuning process, and coarse-grained optimization. To address these challenges, we propose a novel two-stage noisy learning framework that enables instance-level optimization through a dynamically weighted loss function, avoiding hyperparameter tuning. To obtain stable and accurate information about noise modeling, we introduce a simple yet effective metric, termed $\textit{wrong event}$, which dynamically models the cleanliness and difficulty of individual samples while maintaining computational costs. Our framework first collects $\textit{wrong event}$ information and builds a strong base model. Then we perform noise-robust training on the base model, using a probabilistic model to handle the $\textit{wrong event}$ information of samples. Experiments on six synthetic and real-world LNL benchmarks demonstrate our method surpasses state-of-the-art methods in performance, achieves a nearly 75\% reduction in storage and computational time, strongly improving model scalability. Our code is available at https: //github. com/iTheresaApocalypse/IDO.

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AIIM Journal 2025 Journal Article

Online continuous learning of users suicidal risk on social media

Lei Cao
Ling Feng
Yang Ding
Huijun Zhang
Xin Wang
Kaisheng Zeng
Yi Dai

Suicide is a tragedy for family and society. With social media becoming an integral part of people’s life nowadays, assessing suicidal risk based on one’s social media behavior has drawn increasing research attentions. The majority of the works trained a machine learning model to classify user’s suicidal risk severity level in a batch learning setting on the entire training data. This is not a timely and scalable solution in the context of social media where new data arrives sequentially in a stream form. In this study, we formulate and address the continuous suicidal risk assessment problem through a three-layered joint memory network, consisting of a short-term personal memory and long-term personal and global memories. Unlike existing methods that rely on static classification, our model supports real-time, continuous learning from users’ emotional and behavioral dynamics without the need for full retraining. This allows for personalized and adaptive risk tracking over time. We also present a way to continuously capture users’ personal features and integrate them in suicidal risk assessment. The performance on the constructed dataset containing 95 suicidal and 95 non-suicidal social media users shows that 96% of accuracy can be achieved with the proposed method.

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NeurIPS Conference 2025 Conference Paper

Projection-Manifold Regularized Latent Diffusion for Robust General Image Fusion

Lei Cao
Hao Zhang
Chunyu Li
Jiayi Ma

This study proposes PDFuse, a robust, general training-free image fusion framework built on pre-trained latent diffusion models with projection–manifold regularization. By redefining fusion as a diffusion inference process constrained by multiple source images, PDFuse can adapt to varied image modalities and produce high-fidelity outputs utilizing the diffusion prior. To ensure both source consistency and full utilization of generative priors, we develop novel projection–manifold regularization, which consists of two core mechanisms. On the one hand, the Multi-source Information Consistency Projection (MICP) establishes a projection system between diffusion latent representations and source images, solved efficiently via conjugate gradients to inject multi-source information into the inference. On the other hand, the Latent Manifold-preservation Guidance (LMG) aligns the latent distribution of diffusion variables with that of the sources, guiding generation to respect the model’s manifold prior. By alternating these mechanisms, PDFuse strikes an optimal balance between fidelity and generative quality, achieving superior fusion performance across diverse tasks. Moreover, PDFuse constructs a canonical interference operator set. It synergistically incorporates it into the aforementioned dual mechanisms, effectively leveraging generative priors to address various degradation issues during the fusion process without requiring clean data for supervising training. Extensive experimental evidence substantiates that PDFuse achieves highly competitive performance across diverse image fusion tasks. The code is publicly available at https: //github. com/Leiii-Cao/PDFuse.

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NeurIPS Conference 2024 Conference Paper

Text-DiFuse: An Interactive Multi-Modal Image Fusion Framework based on Text-modulated Diffusion Model

Hao Zhang
Lei Cao
Jaiyi Ma

Existing multi-modal image fusion methods fail to address the compound degradations presented in source images, resulting in fusion images plagued by noise, color bias, improper exposure, etc. Additionally, these methods often overlook the specificity of foreground objects, weakening the salience of the objects of interest within the fused images. To address these challenges, this study proposes a novel interactive multi-modal image fusion framework based on the text-modulated diffusion model, called Text-DiFuse. First, this framework integrates feature-level information integration into the diffusion process, allowing adaptive degradation removal and multi-modal information fusion. This is the first attempt to deeply and explicitly embed information fusion within the diffusion process, effectively addressing compound degradation in image fusion. Second, by embedding the combination of the text and zero-shot location model into the diffusion fusion process, a text-controlled fusion re-modulation strategy is developed. This enables user-customized text control to improve fusion performance and highlight foreground objects in the fused images. Extensive experiments on diverse public datasets show that our Text-DiFuse achieves state-of-the-art fusion performance across various scenarios with complex degradation. Moreover, the semantic segmentation experiment validates the significant enhancement in semantic performance achieved by our text-controlled fusion re-modulation strategy. The code is publicly available at https: //github. com/Leiii-Cao/Text-DiFuse.

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JBHI Journal 2023 Journal Article

Continuous Stress Detection Based on Social Media

Yang Ding
Ling Feng
Lei Cao
Yi Dai
Xin Wang
Huijun Zhang
Ningyun Li
Kaisheng Zeng

Leveraging social media for stress detection has been growing attention in recent years. Most relevant studies so far concentrated on training a stress detection model on the entire data in a closed environment, and did not continuously incorporate new information into the already established models but instead regularly reconstruct a new model from scratch. In this study, we formulate a social media based continuous stress detection task with two particular questions to be addressed: (1) when to adapt a learned stress detection model? and (2) how to adapt a learned stress detection model? We design a protocol to quantify the conditions that trigger model's adaptation, and develop a layer-inheritance based knowledge distillation method to continually adapt the learned stress detection model to incoming data, while retaining the knowledge gained previously. The experimental results on a constructed dataset containing 69 users on Tencent Weibo validate the effectiveness of the proposed adaptive layer-inheritance based knowledge distillation method, achieving 86. 32% and 91. 56% of accuracy in 3-label and 2-label continuous stress detection. Implications and further possible improvements are also discussed at the end of the article.

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AIIM Journal 2023 Journal Article

GCLR: A self-supervised representation learning pretext task for glomerular filtration barrier segmentation in TEM images

Guoyu Lin
Zhentai Zhang
Kaixing Long
Yiwen Zhang
Yanmeng Lu
Jian Geng
Zhitao Zhou
Qianjin Feng

Automatic segmentation of the three substructures of glomerular filtration barrier (GFB) in transmission electron microscopy (TEM) images holds immense potential for aiding pathologists in renal disease diagnosis. However, the labor-intensive nature of manual annotations limits the training data for a fully-supervised deep learning model. Addressing this, our study harnesses self-supervised representation learning (SSRL) to utilize vast unlabeled data and mitigate annotation scarcity. Our innovation, GCLR, is a hybrid pixel-level pretext task tailored for GFB segmentation, integrating two subtasks: global clustering (GC) and local restoration (LR). GC captures the overall GFB by learning global context representations, while LR refines three substructures by learning local detail representations. Experiments on 18, 928 unlabeled glomerular TEM images for self-supervised pre-training and 311 labeled ones for fine-tuning demonstrate that our proposed GCLR obtains the state-of-the-art segmentation results for all three substructures of GFB with the Dice similarity coefficient of 86. 56 ± 0. 16%, 75. 56 ± 0. 36%, and 79. 41 ± 0. 16%, respectively, compared with other representative self-supervised pretext tasks. Our proposed GCLR also outperforms the fully-supervised pre-training methods based on the three large-scale public datasets – MitoEM, COCO, and ImageNet – with less training data and time.

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JBHI Journal 2022 Journal Article

Category-Aware Chronic Stress Detection on Microblogs

Lei Cao
Huijun Zhang
Ningyun Li
Xin Wang
Wisong Ri
Ling Feng

People today live a stressful life. Compared with acute stress, long-term chronic stress is more harmful, and may cause or exacerbate many serious health problems, including high blood pressure, heart disease, chronic pain, and mental diseases. With social media becoming an integral part of our daily lives for information sharing and self-expression, detecting category-aware long-standing chronic stress from a large volume of historic open posts made by social media users is possible. In this study, we construct a data set containing 971 chronically stressed users with totally 54 546 open posts on Sina microblog from July 5, 2018 to December 1, 2019, and design two techniques for category-aware chronic stress detection: (1) a stress-oriented word embedding on the basis of an existing pre-trained word embedding, aiming to strengthen the sensibility of stress-related expressions for linguistic post analysis; (2) a multi-attention model with three layers (i. e. , category-attention layer, posts self-attention layer, and category-specific post attention layer), aiming to capture inter-relevance from a sequence of posts and infer long-term stress categories and stress levels. The experimental results show that the proposed multi-attention model equipped with the stress-oriented word embedding can achieve 80. 65% accuracy in detecting category-aware stress levels, 86. 49% accuracy in detecting chronic stress levels only, and 93. 07% accuracy in detecting chronic stress categories only. Limitations and implications of the study are also discussed at the end of the paper.

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YNICL Journal 2022 Journal Article

Fornix alterations induce the disruption of default mode network in patients with adamantinomatous craniopharyngiomas

Jie Kang
Lei Cao
Taoyang Yuan
Lu Jin
Yanjiao He
Xing Liu
Cuiping Zhang
Nan Chen

Adamantinomatous craniopharyngioma (ACPs) are rare embryonic tumors and often involve the hypothalamus. The underlying neural substrate of the hypothalamic involvement (HI)-related cognitive decline in patients with ACP is still unclear. We aimed to combine the multi-modal neuroimaging and histological characteristics of the ACP to explore the potential neural substrate of the HI-related cognitive decline. 45 patients with primary ACPs (invasive, 23; noninvasive, 22) and 52 healthy control subjects (HCs) were admitted to the cross-sectional study. No significant difference in cognitive domains was observed between HCs and patients with noninvasive ACPs (NACP). Patients with invasive ACPs (IACP) showed significantly lower working memory performance (WM, p = 0.002) than patients with NACP. The WM decline was correlated with the disruption of the medial temporal lobe (MTL) subsystem in the default mode network (DMN) (r = 0.45, p = 0.004). The increased radial diffusivity of the fornix, indicating demyelinating process, was correlated with the disruption of the MTL subsystem (r = -0.48, p = 0.002). Our study demonstrated that the fornix alterations link DMN disruption to HI-related cognitive decline in patients with ACPs. ACPs that invade the hypothalamus can provide a natural disease model to investigate the potential neural substrate of HI-related cognitive decline.

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AAAI Conference 2019 Short Paper

Deep Reinforcement Learning via Past-Success Directed Exploration

Xiaoming Liu
Zhixiong Xu
Lei Cao
Xiliang Chen
Kai Kang

The balance between exploration and exploitation has always been a core challenge in reinforcement learning. This paper proposes “past-success exploration strategy combined with Softmax action selection”(PSE-Softmax) as an adaptive control method for taking advantage of the characteristics of the online learning process of the agent to adapt exploration parameters dynamically. The proposed strategy is tested on OpenAI Gym with discrete and continuous control tasks, and the experimental results show that PSE-Softmax strategy delivers better performance than deep reinforcement learning algorithms with basic exploration strategies.

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