Author name cluster

Qingchen Zhang

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

12 papers

1 author row

EAAI Journal 2026 Journal Article

An output perturbation method based on few-shot learning with data augmentation for lung cancer classification

Xiangfei Zhang
Qingchen Zhang

Recently, using deep learning for the classification of computed tomography (CT) images has emerged as a promising approach for lung cancer classification. However, training deep learning models requires a large amount of data, and collecting a significant number of lung cancer CT images is a challenging task. Moreover, deep learning models are susceptible to privacy attacks, such as membership inference attacks (MIAs), which limit their application in the medical field. To address these issues, we propose a output perturbation method based on few-shot learning with data augmentation in this work. Specifically, to tackle the problem of insufficient data, we utilize data augmentation techniques to enrich the samples. We embed the original and augmented data using an encoder, and then perform a weighted fusion of these features. The fused features are subsequently input into a Multi-Layer Perceptron (MLP) to obtain the final embedded features. Furthermore, to prevent MIAs, we propose a differential privacy (DP)-based output perturbation strategy, which adaptively adds DP noise to the embedding vectors output by the MLP. Outputs with larger absolute values receive more noise, while those with smaller absolute values receive less noise. Experiments are conducted on two publicly available datasets, and the results showed that the proposed method in the DP scenario has significant advantages over the baseline method in lung cancer classification.

Details DOI

AAAI Conference 2026 Conference Paper

GATCL: An Adaptive Contrastive Learning Framework Based on MHGAT for Spatial Domain Identification in Spatial Transcriptomics

Shilin Zhang
Weiliang Huo
Qingchen Zhang
Xiulong Liu

Recent advances in spatial transcriptomics have enabled the simultaneous measurement of gene expression profiles and spatial location information, offering a more comprehensive and in-depth view for studying the tissue microenvironment. Spatial domain identification is a crucial step in analyzing spatial transcriptomics. However, current methods have poor accuracy and visualization because they lack self-adaptability to different tissue data, and moreover, they cannot effectively extract spatial location information. To address these issues, we propose an adaptive graph contrastive learning framework based on multi-head graph attention networks (GATCL) for spatial domain identification. Specifically, we design a data augmentation module to mask and shuffle the pre-processed gene expression data to generate more differentiated negative samples. In addition, we construct the multi-head graph attention networks (MHGAT) to encode gene expression profiles and spatial location information. More importantly, we design an adaptive graph contrastive learning model that works both with positive and negative samples from spatial transcriptomics. We introduce the attention pooling mechanism to dynamically and adaptively aggregate the spots' neighborhood information, and to improve the model's generalization ability for different spatial transcriptomics data. Furthermore, we design a discriminator that adds spectral normalization to bilinear functions. Experimental results on DLPFC, breast cancer, and mouse somatosensory cortex datasets demonstrate that the average Adjusted Rand Index (ARI) scores are 0.5746, 0.6182, and 0.6496, respectively, significantly outperforming baseline methods. More importantly, GATCL provides a more detailed visualization of different spatial transcriptomics data.

PDF Details DOI

AAAI Conference 2026 Conference Paper

SAMGTD: Spatial-Aware Masked Graph Transformer-Diffusion Model for Enhanced Cell Type Deconvolution in Spatial Transcriptomics

Shilin Zhang
Suixue Wang
Qingchen Zhang
Xiulong Liu

Recent advances in spatial transcriptomics have enabled the integration of gene expression profiles with precise spatial coordinates, which have facilitated the exploration of tumor occurrence and development mechanisms, as well as the development of more effective targeted and immunotherapy approaches for tumor treatment. Deciphering cell type represents a critical challenge in spatial transcriptomics research. Existing methods are limited by the pervasive “dropout” events in spatial transcriptomics, hindering their ability to fully capture the relationship between spatial location and gene expression, thereby compromising the performance of cell type deconvolution. To address these limitations, we propose a spatial-aware masked graph transformer-diffusion model (SAMGTD) for enhanced cell type deconvolution in spatial transcriptomics. For spatial transcriptomics, the masked graph transformer model is designed to adaptively capture complex dependencies between spatial locations and gene expression. It employs a masking strategy that guides the model to focus on important local information during training, while the multi-head attention mechanism captures global context. More importantly, the spatial diffusion model is constructed to achieve the dual enhancement of spatial transcriptomics, including denoising and data imputation. It incorporates the multi-head attention mechanism and residual blocks, effectively addressing the “dropout” issue commonly encountered in spatial transcriptomics. For scRNA-seq, we construct a variational autoencoder to reduce noise interference while preserving key gene expression information. Finally, we construct a spatial-aware contrastive learning model to integrate scRNA-seq and spatial transcriptomics for cell type deconvolution. Experiments conducted on three datasets demonstrate that SAMGTD outperforms baseline methods.

PDF Details DOI

AAAI Conference 2026 Conference Paper

SSL-CST: Cell Segmentation for Single-Cell Spatial Transcriptome Based on Self-Supervised Learning

Weiliang Huo
Shilin Zhang
Suixue Wang
Qingchen Zhang

The continuous advancements in life science technology have enabled spatial transcriptome technology to achieve an impressive level of resolution at the single-cell level. This technology has emerged as a crucial method for studying the cellular composition and differentiation states of tissues, investigating cell-cell interactions, and unraveling the molecular mechanisms underlying diseases and developmental processes. A key component in this analysis is the accurate segmentation of cells. However, existing segmentation methods often fail to fully leverage the valuable information provided by spatial transcriptomics, leading to inaccurate cell segmentation. In this study, we introduce SSL-CST, a cell segmentation for single-cell spatial transcriptome method based on self-supervised learning. SSL-CST employs a pre-trained model for foundational contour segmentation. Following the denoising process, it utilizes a self-supervised neural network to correct the cell boundaries to obtain accurate cell boundaries. Through this approach, SSL-CST outperforms other state-of-the-art methods in various tests conducted on multiple datasets. The improved segmentation provided by SSL-CST further enhances the analysis of single-cell spatial expression, providing effective tools for biological discovery.

PDF Details DOI

IJCAI Conference 2025 Conference Paper

CSF-GAN: Cross-modal Semantic Fusion-based Generative Adversarial Network for Text-guided Image Inpainting

Shilin Zhang
Suixue Wang
Qingchen Zhang
Liang Zhao
Weiliang Huo
Sijia Hou
Chunjiang Fu

Most visual-guided image inpainting methods based on generative adversarial networks (GANs) struggle when the missing region has weak correlations with the surrounding visual context. Recently, diffusion-based methods guided by textual context have been proposed to address this limitation by leveraging additional semantic information to restore corrupted objects. However, these models typically involve more parameters and exhibit slower generation speeds compared to GAN-based approaches. To address this problem, we propose a novel text-guided image inpainting model, the cross-modal semantic fusion generative adversarial network (CSF-GAN). CSF-GAN is designed as a one-stage GAN with the following key contributions. First, a novel semantic fusion module (SFM) is introduced to integrate sentence- and word-level textual context into the inpainting process, enabling more effective guidance from multi-granularity semantic information. Second, a newly designed word-level local discriminator provides detailed feedback to the generator, enhancing the accuracy of generated content in alignment with word-level semantics. Third, two loss functions, the inpainting loss and edge loss, are employed to enhance both structural coherence and textural realism in the generated results. Extensive experiments on two benchmark datasets demonstrate that CSF-GAN outperforms state-of-the-art methods.

PDF Details DOI

IJCAI Conference 2025 Conference Paper

Dual Robust Unbiased Multi-View Clustering for Incomplete and Unpaired Information

Liang Zhao
Ziyue Wang
Chuanye He
Qingchen Zhang
Bo Xu

Recently, multi-view data has gradually attracted attention. However, real-world applications often face Partial View-aligned Problem (PVP) and Partially Sample-missing Problem (PSP) due to data loss or corruption. Existing methods addressing PVP typically focus only on learning from the information of aligned data, while ignoring unaligned data where samples exist but lack alignment relationships. This introduces PSP, which does not inherently exist in the data, leading to biased learning of the data's information. For PSP, due to varying degrees of missing data, incomplete spatial structures can cause clustering centers-shifted problem, resulting in the model learning incorrect correspondences and biased spatial structures. To tackle them, we propose a novel method called Dual Robust Unbiased Multi-View Clustering for Incomplete and Unpaired Information (DRUMVC). To our knowledge, this is the first noise-robust and unbiased multi-view clustering method capable of simultaneously addressing both PVP and PSP. Specifically, DRUMVC leverages aligned and complete samples as a bridge to construct high-quality correspondences for samples lacking cross-view relationship information due to PVP or PSP. Additionally, we employ a dual noise-robust contrastive learning loss to mitigate the impact of noise potentially introduced during the pair construction. Experiments on several challenging datasets demonstrate the superiority of our proposed method.

PDF Details DOI

IJCAI Conference 2025 Conference Paper

EchoGPT: An Interactive Cardiac Function Assessment Model for Echocardiogram Videos

Bo Xu
Quanhao Zhu
Qingchen Zhang
Mengmeng Wang
Liang Zhao
Hongfei Lin
Jing Ren
Feng Xia

With the development of wearable cardiac ultrasound devices, it is no longer sufficient to solely rely on doctors for diagnosing long-term echocardiogram videos. Automated diagnosis of echocardiogram videos has now become a research hotspot. Existing studies only analyze echocardiogram video through discriminative models, which have limited question-answering capabilities. Therefore, this study innovatively proposes a large language model with cardiac ultrasound diagnostic capabilities—EchoGPT. EchoGPT integrates the robust communication and comprehension capabilities of large language models (LLMs) with the diagnostic prowess of traditional medical models, empowering patients to obtain accurate medical indicator data and comprehend their health conditions through interactive questioning with the model. The model is capable of local deployment on personal computers, effectively safe guarding user privacy. EchoGPT operates through three main components: left ventricle segmentation, left ventricular ejection fraction LVEF prediction, and finetuning of video-text LLMs. Experimental results demonstrate EchoGPT’s superior accuracy in predicting LVEF compared to other models, and positive feedback from professional physicians through questionnaire surveys, validating its potential in practical applications. The demo is available at https: //github. com/zhuqh19/EchoGPT.

PDF Details DOI

JBHI Journal 2025 Journal Article

GCNLA: Inferring Cell-Cell Interactions From Spatial Transcriptomics With Long Short-Term Memory and Graph Convolutional Networks

Chao Yang
Xiuhao Fu
Zhenjie Luo
Leyi Wei
Jingbing Li
Feifei Cui
Quan Zou
Qingchen Zhang

Spatial transcriptomics analysis methods offer an opportunity to investigate highly diverse biological tissues. Cell-cell communication is fundamental for maintaining physiological homeostasis in organisms and coordinating complex biological processes. Identifying cell-cell interactions is critical for understanding cellular activities. The interaction of a cell with other cells depends on several factors, and most of the existing methods that consider only gene expression information of neighbouring cells and spatial location information are somewhat limited. In this paper, we propose a network architecture based on graph convolution network and long short-term memory attention module-GCNLA, which contains graph convolution layer, long short-term memory network, attention module, and residual connections. GCNLA not only learns the spatial structure of cells but also captures interaction information between distal cells, the attention module further extracting and enhancing features related to cell-cell interactions. Finally, the inner product decoding calculates the cosine similarity, which is used to infer cell-cell interactions. In addition, GCNLA is capable of reconstructing the complete cell-cell interaction network. The experimental results on seqFISH and MERFISH demonstrate that the GCNLA network structure has better robustness and noise immunity. The potential features learned by GCNLA enable other downstream analyses, including single-cell resolution cell clustering based on spatial information resolving cell heterogeneity.

Details DOI

IJCAI Conference 2025 Conference Paper

Higher-order Logical Knowledge Representation Learning

Suixue Wang
Weiliang Huo
Shilin Zhang
Qingchen Zhang

Real-world knowledge graphs abound with higher-order logical relations that simple triples, limited to pairwise connections, fail to represent. Thus, capturing higher-order logical relations involving multiple entities has garnered significant attention. However, existing methods ignore the structural information in higher-order relations. To this end, we propose a higher-order logical knowledge representation learning method, named LORE, which leverages network motifs, the patterns/subgraphs that naturally capture the structural information in graphs, to extract higher-order features and ultimately, learn effective representations of knowledge graphs. Compared to existing approaches, LORE aggregates the attribute features of entities with the extracted higher-order logical relations to form enhanced representations of knowledge graphs. In particular, three aggregators (i. e. , Hadamard, Connection, and Summation) are proposed and employed. Extensive experiments have been conducted on six real-world datasets for two downstream tasks (i. e. , entity classification and link prediction). The results show that LORE outperforms baselines significantly and consistently.

PDF Details DOI

IJCAI Conference 2025 Conference Paper

MASTER: A Multi-granularity Invariant Structure Clustering Scheme for Multi-view Clustering

Suixue Wang
Shilin Zhang
Qingchen Zhang
Peng Li
Weiliang Huo

Deep multi-view clustering has attracted increasing attention in the pattern mining of data. However, most of them perform self-learning mechanisms in a single space, ignoring the fruitful structural information hidden in different-level feature spaces. Meanwhile, they conduct the reconstruction constraint to learn generalized representations of samples, failing to explore the discriminative ability of complementary and consistent information. To address the challenges, a multi-granularity invariant structure clustering scheme (MASTER) is proposed to define a bottom-up process that extracts multi-level information in sample, neighborhood, and category granularities from low-level, high-level, and semantics feature space, respectively. Specifically, it leverages the self-learning reconstruction with information-theoretic overclustering to capture invariant sample structure in the low-level feature space. Then, it models data diffusion of the clustering process in the reliable neighborhood to capture invariant local structure in the high-level feature space. Meanwhile, it defines dual divergences induced by the space geometry to capture invariant global structure in the semantics space. Finally, extensive experiments on 8 real-world datasets show that MASTER achieves state-of-the-art performance compared to 11 baselines.

PDF Details DOI

IJCAI Conference 2025 Conference Paper

POMP: Pathology-omics Multimodal Pre-training Framework for Cancer Survival Prediction

Suixue Wang
Shilin Zhang
Huiyuan Lai
Weiliang Huo
Qingchen Zhang

Cancer survival prediction is an important direction in precision medicine, aiming to help clinicians tailor treatment regimens for patients. With the rapid development of high-throughput sequencing and computational pathology technologies, survival prediction has shifted from clinical features to joint modeling of multi-omics data and pathology images. However, existing multimodal learning methods struggle to effectively learn pathology-omics interactions due to the lack of proper alignment of multimodal data before fusion. In this paper, we propose POMP, a pathology-omics multimodal pre-training framework jointly learned with three training tasks for integrating pathological images and omics data for cancer survival prediction. To better perform cross-modal learning, we introduce a pathology-omics contrastive learning method to align the pathology and omics information. POMP leverages the principle of pre-trained models and explores the benefit of aligning multimodal information from the same patient, achieving state-of-the-art results on six cancer datasets from the Cancer Genome Atlas (TCGA). We also show that our contrastive learning method allows us to exploit the cosine similarity of pathological images and omics data as the survival risk score, which can further boost prediction performance compared with other commonly used methods. The code is available at https: //github. com/SuixueWang/POMP.

PDF Details DOI

JBHI Journal 2024 Journal Article

Guest Editorial AI-Empowered Internet of Things for Data-Driven Psychophysiological Computing and Patient Monitoring

Kai Fang
Wei Wang
Marcin Woźniak
Qingchen Zhang
Keping Yu
Junxin Chen
Amr Tolba
Leo Zhang

As The cornerstone of human health, physical and mental well-being are intricately linked, influencing both an individual's physical condition and their emotional state [1]. Chronic diseases such as hypertension and diabetes can have a significant impact on mental health, leading to anxiety and depression [2]. Similarly, psychological problems such as stress, anxiety, and depression can weaken the immune system, making individuals more susceptible to physical illnesses. In recent years, the rapid development of technology has brought exciting new possibilities to the field of physical and psychological health. The Internet of Things (IoT) and artificial intelligence (AI) have shown great potential in building a comprehensive health management system that empowers individuals to take a more proactive role in their well-being.

Details DOI