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IEEE Transactions on computational Biology and Bioinformatics 2025年第1期22卷 271-284页

作者： Zhenqiu Shu Min Xia Kaiwen Tan Zhengtao Yu Xiao-Jun Wu Faculty of Information Engineering and Automation Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Kunming China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi China

Clustering is a crucial step in single-cell RNA sequencing (scRNA-seq) data analysis, facilitating the discovery of new cell types and the grouping of similar cells. Recently, graph convolutional networks (GCNs) have gained prominence in scRNA-seq data clustering because they effectively learn cell representations by capturing the relationship between cells. However, GCNs are sensitive to noise in scRNA-seq data and are prone to over-smoothing, resulting in the loss of cell-specific information. To overcome these challenges, we propose sigRGCN, a robust residual graph convolutional network for scRNA-seq data clustering. Specifically, we first construct a disturbed cell graph by injecting noise into a cell graph constructed from scRNA-seq data. Then, we design a graph structure optimization graph convolutional network to eliminate the impact of noise in the disturbed cell graph. It significantly improves the robustness of the proposed model in real scRNA-seq data clustering tasks. After that, we utilize a $L$ -layers residual graph convolutional network to alleviate the over-smoothing problem. It allows our model to effectively capture higher-order relationships between cells, leading to better cell representations. Finally, we employ a self-supervised manner to optimize our model. The experimental results on nine real scRNA-seq datasets show that our proposed model demonstrates competitive performance in real clustering tasks.

关键词： Noise Data models Convolution Optimization Correlation Autoencoders RNA Graph convolutional networks Sequential analysis Noise reduction

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Multisource and Hidden Source-Based Knowledge Transfer for Solving Dynamic Multiobjective Optimization Problems

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IEEE Transactions on Evolutionary Computation 2025年

作者： Song, Wei Liu, Zhi Yu, Jian Sun, Xiaoyan Jin, Yaochu Lai, Khin Wee Jiangnan University School of Artificial Intelligence and Computer Science Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Wuxi214122 China Westlake University Trustworth and General Artificial Intelligence Laboratory School of Engineering Hangzhou331712 China Universiti Malaya Department of Biomedical Engineering Kuala Lumpur50603 Malaysia

Recently, transfer learning-based dynamic multiobjective optimization algorithms (TL-DMOAs) have been shown to be very promising in solving dynamic multiobjective optimization problems (DMOPs). However, it is difficult for them to model knowledge capable of delineating the Pareto optimal solutions (POSs) found in each historical environment, because the POSs’ distribution cannot be adequately reflected. Besides, existing TL-DMOAs normally focus on acquiring knowledge from historical environments, but neglect correlations behind them for excavating potential knowledge, restricting the performance in generating high-quality initial populations (HIPs). To address these issues, herein a DMOA with multisource and hidden source-based knowledge transfer (DMOA-MHKT) is proposed. First, we design a knowledge extraction strategy by introducing mean shift, a nonparametric clustering method, to cluster the historical POSs. As clusters’ representatives, the cluster centers are considered to represent environmental knowledge, because they can adequately reflect the POSs’ distribution. Second, the most similar historical environment through environmental match and the last one are selected as two explicit sources. In the former source the POSs’ cluster centers are treated as its knowledge. By contrast, based on the POSs’ cluster centers and knee points in the latter source, a scoring method is designed to generate environmental knowledge by depicting the dynamics between two continuous environments. Third, after aligning knowledge of the explicit sources, a hidden source is learned by excavating correlations and potential knowledge behind them, facilitating the generalization enhancement in generating HIPs. The experimental results especially performance comparisons with seven state-of-the-art DMOAs demonstrate that DMOA-MHKT brings significant improvements in solving DMOPs. © 2025 IEEE. All rights reserved.

关键词： Contrastive Learning

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EACNet:Ensemble adversarial co-training neural network for handling missing modalities in MRI images for brain tumor segmentation

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Journal of Measurement Science and Instrumentation 2025年第1期16卷 11-25页

作者： RAMADHAN Amran Juma CHEN Jing PENG Junlan School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi 214122China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computing Intelligence Jiangnan UniversityWuxi 214122China

Brain tumor segmentation is critical in clinical diagnosis and treatment *** methods for brain tumor segmentation with missing modalities often struggle when dealing with multiple missing modalities,a common scenario in real-world clinical *** methods primarily focus on handling a single missing modality at a time,making them insufficiently robust for the additional complexity encountered with incomplete data containing various missing modality ***,most existing methods rely on single models,which may limit their performance and increase the risk of overfitting the training *** work proposes a novel method called the ensemble adversarial co-training neural network(EACNet)for accurate brain tumor segmentation from multi-modal magnetic resonance imaging(MRI)scans with multiple missing *** proposed method consists of three key modules:the ensemble of pre-trained models,which captures diverse feature representations from the MRI data by employing an ensemble of pre-trained models;adversarial learning,which leverages a competitive training approach involving two models;a generator model,which creates realistic missing data,while sub-networks acting as discriminators learn to distinguish real data from the generated“fake”***-training framework utilizes the information extracted by the multimodal path(trained on complete scans)to guide the learning process in the path handling missing *** model potentially compensates for missing information through co-training interactions by exploiting the relationships between available modalities and the tumor segmentation *** was evaluated on the BraTS2018 and BraTS2020 challenge datasets and achieved state-of-the-art and competitive performance ***,the segmentation results for the whole tumor(WT)dice similarity coefficient(DSC)reached 89.27%,surpassing the performance of existing *** analysis suggests that the ensemble appr

关键词： deep learning magnetic resonance imaging(MRI) medical image analysis semantic segmentation segmentation accuracy image synthesis

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Dual-path Interactive U-Net for Unsupervised Hyperspectral Image Super-Resolution

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 11751-11766页

作者： Deng, Wenchen Liu, Jianjun Yang, Jinlong Wu, Zebin Xiao, Liang Jiangnan University Jiangsu Provincial Engineering Laboratory for Pattern Recognition and Computational Intelligence School of Artificial Intelligence and Computer Science Wuxi 214122 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing 210094 China

Combining low-spatial-resolution hyperspectral image (LrHSI) with high-spatial-resolution multispectral image (HrMSI) serves as an effective strategy for enhancing the spatial fidelity of LrHSI. Nevertheless, most existing methods still face challenges in effectively leveraging the complementary information between the two distinct modalities and maintaining internal consistency, leading to suboptimal fusion results. Previous studies have demonstrated that U-shaped networks can capture spatial structural features within images. Inspired by this, we propose a dual-path interactive U-Net architecture to preserve spatial and spectral integrity. Specifically, we use a standard U-Net and a reversed U-Net as the backbone to extract image information and generate abundance maps of the input images. By enabling interaction between the encoders and decoders of both U-Nets, our architecture integrates information across different scales and modes, leading to enhanced fusion results. To further improve the feature extraction capability, we construct a multi-mode decomposition and reconstruction module, which adaptively fuses the features of LrHSI and HrMSI. This module extracts and combines correlations between the images through Canonical Polyadic decomposition and attention mechanism, capturing global features across different modes. Additionally, we design a weight-sharing U-Net that leverages the similarities and differences between two abundance maps, ensuring internal consistency while reducing computational cost. Thorough evaluations conducted using four publicly available datasets, along with one real-world dataset, and under various noise conditions confirm the validity of our proposed model. © 2008-2012 IEEE.

关键词： Hyperspectral image (HSI) super-resolution U-Net unsupervised learning

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Estimating Difficulty Score of Visual Search in Images for Semi-supervised Object Detection 1

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16th Pacific Rim Knowledge Acquisition Workshop, PKAW 2019 held in conjunction with the 16th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2019

作者： Ma, Dongliang Zhang, Haipeng Wu, Hao Zhang, Tao Sun, Jun JiangSu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence JiangNan University Wuxi China

ISBN: (数字)9783030306397

ISBN: (纸本)9783030306380

Humans can intuitively understand the content of images, and often reach a consensus that some images are more difficult to visual search tasks than others. However, this is quite challenging for computers as it is a subjective task which may be influenced by human emotional factors. Instead of focusing on how the models make reactions on datasets, our method has a capability of assigning scores to samples respectively within a dataset that estimating the difficulty of visual search tasks. Our model shows better performance for predicting visual search difficulty scores of samples produced by human annotators in PASCAL VOC2012. Eventually, we demostrate with experiments that our method has an ability of selecting suitable samples to improve the performance of detectors in a semi-supervised task. © 2019, Springer Nature Switzerland AG.

关键词： Object detection

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A fast GA for automatically evolving CNN architectures 20

A fast GA for automatically evolving CNN architectures

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2020 Genetic and Evolutionary Computation Conference, GECCO 2020

作者： Hong, Zhou Fang, Wei Sun, Jun Wu, Xiaojun Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi China

ISBN: (纸本)9781450371278

Convolutional neural networks (CNNs) have been proved to be effective models to solve a series of challenging computer vision tasks. However, designing CNN architectures with good performance is still a challenging task. Automatic CNN architecture search algorithms have been proposed in recent years which can find competitive CNN architectures without manual intervention. But automatic search algorithms usually consume considerable computational time and resources. In addition, they only use deep blocks and ignore wide blocks of CNNs, which limits the performance of evolved CNNs. In order to address the above issues, a fast approach for automatically evolving CNN architectures based on deep and wide blocks (FAE-CNN) is proposed through genetic algorithms in this paper. In FAE-CNN, a refined fitness evaluation method based on divided datasets is designed with the purpose to speed up the running time. By introducing the Inception Block, FAE-CNN can evolve optimal CNN architectures from both deep and wide directions. Experimental results on CIFAR10 and CIFAR100 show that FAE-CNN can automatically design CNNs with flexible architectures and better performance in a very short running time. © 2020 Owner/Author.

关键词： Genetic algorithms

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Component SPD matrices： A low-dimensional discriminative data descriptor for image set classification

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computational Visual Media 2018年第3期4卷 245-252页

作者： Kai-Xuan Chen Xiao-Jun Wu School of loT Engineering 3iangnan University Wuxi 214122 China. Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi 214122 China.

In pattern recognition,the task of image set classification has often been performed by representing data using symmetric positive definite(SPD)matrices,in conjunction with the metric of the resulting Riemannian *** this paper,we propose a new data representation framework for image sets which we call component symmetric positive definite representation(CSPD).Firstly,we obtain sub-image sets by dividing the images in the set into square blocks of the same size,and use a traditional SPD model to describe ***,we use the Riemannian kernel to determine similarities of corresponding subimage ***,the CSPD matrix appears in the form of the kernel matrix for all the sub-image sets;its i,j-th entry measures the similarity between the i-th and j-th sub-image *** Riemannian kernel is shown to satisfy Mercer’s theorem,so the CSPD matrix is symmetric and positive definite,and also lies on a Riemannian *** on three benchmark datasets shows that CSPD is both lower-dimensional and more discriminative data descriptor than standard SPD for the task of image set classification.

关键词： symmetric positive definite (SPD) matricesRiemannian kernel image classificationRiemannian manifold

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VLNet: A Multi-task Network for Joint Vehicle and Lane Detection 12th

VLNet: A Multi-task Network for Joint Vehicle and Lane Det...

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12th International Conference on Image and Graphics, ICIG 2023

作者： Feng, Aiqi Liu, Haodong Xu, Tianyang Zhang, Donglin Wu, Xiao-Jun Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Jiangsu Wuxi214122 China

ISBN: (纸本)9783031463075

Visual perception is a crucial component of autonomous driving. Fully understanding the road environment is conducive to the safe driving of vehicles. However, most existing methods are usually unable to simultaneously accomplish multiple visual perception tasks, limiting the promotion and utilization. To this end, we propose VLNet: a novel and effective multi-task road environment perception network, which can perform vehicle and lane detection simultaneously. Specifically, VLNet consists of a shared feature extraction network and two detection branches for specific tasks. The vehicle detection branch uses a multi-scale method to detect vehicles, and the lane detection branch outputs instance-level segmentation map of lanes. Considering the correlation between vehicles and lanes, we leverage the semantic clues of the lane branch to assist vehicle detection. Compared with single-task networks, the proposed method reduces memory and computing costs effectively. Finally, experiments on the challenging Boxy vehicle detection dataset and the LLAMAS lane detection dataset demonstrate the efficacy of the developed model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Semantics

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MSC-Fuse: An Unsupervised Multi-scale Convolutional Fusion Framework for Infrared and Visible Image 11th

MSC-Fuse: An Unsupervised Multi-scale Convolutional Fusion F...

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11th International Conference on Image and Graphics, ICIG 2021

作者： Chen, Guo-Yang Wu, Xiao-Jun Li, Hui Xu, Tian-Yang Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Jiangsu Wuxi214122 China

ISBN: (纸本)9783030873547

Lacking the labeled data, how to establish an unsupervised learning method is essential for the infrared and visible image fusion task. As such, this article introduces a novel unsupervised learning fusion framework. Our proposed framework consists of three components: encoder, fusion layer, and decoder, respectively. Firstly, an encoder is designed to extract salient features from multiple source images. With the multi-scale convolution modules, the encoder can produce more useful features. Then these features are fused at the fusion layer. Finally, the decoder reconstructs the fused features to generate the fused image. To achieve the unsupervised training of the network, a no-reference quality metric and a pixel-level function are utilized to calculate the loss function. Experimental results show that compared with other fusion methods, our proposed method can achieve better performance in both objective and subjective assessments. © 2021, Springer Nature Switzerland AG.

关键词： Image fusion

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CL-Flow:strengthening the normalizing flows by contrastive learning for better anomaly detection

CL-Flow:strengthening the normalizing flows by contrastive l...

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2024 International Conference on Image, Signal Processing, and pattern recognition, ISPP 2024

作者： Wang, Shunfeng Li, Yueyang Luo, Haichi Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University 1800 Lihu Avenue Jiangsu Wuxi214122 China

ISBN: (纸本)9781510680425

In the anomaly detection field, the scarcity of anomalous samples has directed the current research emphasis towards unsupervised anomaly detection. While these unsupervised anomaly detection methods offer convenience, they also overlook the crucial prior information embedded within anomalous samples. Moreover, among numerous deep learning methods, supervised methods generally exhibit superior performance compared to unsupervised methods. Considering the reasons mentioned above, we propose a self-supervised anomaly detection approach that combines contrastive learning with 2D-Flow to achieve more precise detection outcomes and expedited inference processes. On one hand, we introduce a novel approach to anomaly synthesis, yielding anomalous samples in accordance with authentic industrial scenarios, alongside their surrogate annotations. On the other hand, having obtained a substantial number of anomalous samples, we enhance the 2D-Flow framework by incorporating contrastive learning, leveraging diverse proxy tasks to fine-tune the network. Our approach enables the network to learn more precise mapping relationships from self-generated labels while retaining the lightweight characteristics of the 2D-Flow. Compared to mainstream unsupervised approaches, our selfsupervised method demonstrates superior detection accuracy, fewer additional model parameters, and faster inference speed. Our approach showcases new state-of-the-art results, achieving a performance of 99.6% in image-level AUROC on the MVTecAD dataset and 96.8% in image-level AUROC on the BTAD dataset. © 2024 SPIE.

关键词： Contrastive Learning

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