检索结果-内蒙古大学图书馆

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

A Spectral Diffusion Prior for Hyperspectral Image Super-Resolution

arXiv

引用

arXiv 2023年

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

Fusion-based hyperspectral image (HSI) superresolution aims to produce a high-spatial-resolution HSI by fusing a low-spatial-resolution HSI and a high-spatial-resolution multispectral image. Such a HSI super-resolution process can be modeled as an inverse problem, where the prior knowledge is essential for obtaining the desired solution. Motivated by the success of diffusion models, we propose a novel spectral diffusion prior for fusion-based HSI super-resolution. Specifically, we first investigate the spectrum generation problem and design a spectral diffusion model to model the spectral data distribution. Then, in the framework of maximum a posteriori, we keep the transition information between every two neighboring states during the reverse generative process, and thereby embed the knowledge of trained spectral diffusion model into the fusion problem in the form of a regularization term. At last, we treat each generation step of the final optimization problem as its subproblem, and employ the Adam to solve these subproblems in a reverse sequence. Experimental results conducted on both synthetic and real datasets demonstrate the effectiveness of the proposed approach. The code of the proposed approach will be available on https://***/liuofficial/SDP. Copyright © 2023, The Authors. All rights reserved.

关键词： Inverse problems

来源：评论

学校读者我要写书评

暂无评论

A Deep Decomposition Network for Image Processing: A Case Study of Image Fusion

SSRN

引用

SSRN 2022年

作者： Fu, Yu Xu, Tianyang Wu, Xiao-Jun Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China

Image decomposition into constituent components has many applications in the field of image processing. It aims to extract salient features from the source image for subsequent pattern recognition. In this paper, we propose a new image decomposition method based on a convolutional neural network. It is applied to the task of image fusion. In particular, a pair of infrared and visible light images are decomposed into three high-frequency feature images and a low-frequency feature images respectively. The two sets of feature images are fused using a novel fusion strategy to obtain fused feature maps. The feature maps are subsequently reconstructed to obtain the fused image. Compared with the state-of-the-art fusion methods, the proposed approach achieves betterperformance in both subjective and objective evaluation. © 2022, The Authors. All rights reserved.

关键词： Image fusion

来源：评论

学校读者我要写书评

暂无评论

A Riemannian Residual Learning Mechanism for SPD Network

A Riemannian Residual Learning Mechanism for SPD Network

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Zhenyu Cai Rui Wang Tianyang Xu Xiaojun Wu Josef Kittler School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Wuxi China Centre for Vision Speech and Signal Processing University of Surrey Guildford U.K.

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The generalization of Euclidean network paradigm to the Riemannian manifolds has attracted much attention for offering useful geometric representations in processing manifold-valued data in recent years. However, the information degradation during data compression mapping hinders Riemannian networks from going deeper, and there are very few solutions specifically designed for this problem. Given the remarkable success of deep Residual learning in Euclidean networks, a novel Riemannian residual learning mechanism (RRLM) is proposed in the context of Symmetric Positive Definite (SPD) manifolds, enabling the characterization of deep spatiotemporal features while preserving the manifold properties. Based on RRLM, a stack of SPD manifold-constrained residual-like blocks is designed on the tail of the original SPDNet(backbone) for the sake of conducting deep Riemannian residual learning. For simplicity, we refer to the network architecture introduced above as Riemannian residual SPD network (ResSPDNet). The experimental results achieved on three types of visual classification tasks, i.e., facial emotion recognition, drone recognition, and action recognition, demonstrate that our method can achieve improved accuracy with a deepened network structure.

关键词： Manifolds Learning systems Degradation Emotion recognition Visualization Accuracy Face recognition

来源：评论

学校读者我要写书评

暂无评论

Semantic Feature Integration network for Fine-grained Visual Classification

arXiv

引用

arXiv 2023年

作者： Wang, Hui Li, Yueyang Luo, Haichi Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University 1800 Lihu Avenue Jiangsu Wuxi214000 China College of Internet of Things Engineering Jiangnan University 1800 Lihu Avenue Jiangsu Wuxi214000 China

Fine-Grained Visual Classification (FGVC) is known as a challenging task due to subtle differences among subordinate categories. Many current FGVC approaches focus on identifying and locating discriminative regions by using the attention mechanism, but neglect the presence of unnecessary features that hinder the understanding of object structure. These unnecessary features, including 1) ambiguous parts resulting from the visual similarity in object appearances and 2) noninformative parts (e.g., background noise), can have a significant adverse impact on classification results. In this paper, we propose the Semantic Feature Integration network (SFI-Net) to address the above difficulties. By eliminating unnecessary features and reconstructing the semantic relations among discriminative features, our SFI-Net has achieved satisfying performance. The network consists of two modules: 1) the multi-level feature filter (MFF) module is proposed to remove unnecessary features with different receptive field, and then concatenate the preserved features on pixel level for subsequent disposal;2) the semantic information reconstitution (SIR) module is presented to further establish semantic relations among discriminative features obtained from the MFF module. These two modules are carefully designed to be light-weighted and can be trained end-to-end in a weakly-supervised way. Extensive experiments on four challenging fine-grained benchmarks demonstrate that our proposed SFI-Net achieves the state-of-the-arts performance. Especially, the classification accuracy of our model on CUB-200-2011 and Stanford Dogs reaches 92.64% and 93.03%, respectively. Copyright © 2023, The Authors. All rights reserved.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

A fast GA for automatically evolving CNN architectures 20

A fast GA for automatically evolving CNN architectures

引用

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

来源：评论

学校读者我要写书评

暂无评论

Collaborative Representation for SPD Matrices with Application to Image-Set Classification

arXiv

引用

arXiv 2022年

作者： Chu, Li Wang, Rui Wu, Xiao-Jun The School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University China

Collaborative representation-based classification (CRC) has demonstrated remarkable progress in the past few years because of its closed-form analytical solutions. However, the existing CRC methods are incapable of processing the nonlinear variational information directly. Recent advances illustrate that how to effectively model these nonlinear variational information and learn invariant representations is an open challenge in the community of computer vision and pattern recognition To this end, we try to design a new algorithm to handle this problem. Firstly, the second-order statistic, i.e., covariance matrix is applied to model the original image sets. Due to the space formed by a set of nonsingular covariance matrices is a well-known Symmetric Positive Definite (SPD) manifold, generalising the Euclidean collaborative representation to the SPD manifold is not an easy task. Then, we devise two strategies to cope with this issue. One attempts to embed the SPD manifold-valued data representations into an associated tangent space via the matrix logarithm map. Another is to embed them into a Reproducing Kernel Hilbert Space (RKHS) by utilizing the Riemannian kernel function. After these two treatments, CRC is applicable to the SPD manifold-valued features. The evaluations on four banchmarking datasets justify its effectiveness. © 2022, CC BY.

关键词： Covariance matrix

来源：评论

学校读者我要写书评

暂无评论

General and robust voxel feature learning with Transformer for 3D object detection

引用

Journal of Measurement Science and Instrumentation 2022年第1期13卷 51-60页

作者： LI Yang GE Hongwei Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Wuxi 214122 China School of Artificial Intelligence and Computer Science Jiangnan University Wuxi 214122 China

The self-attention networks and Transformer have dominated machine translation and natural language processing fields,and shown great potential in image vision tasks such as image classification and object *** by the great progress of Transformer,we propose a novel general and robust voxel feature encoder for 3D object detection based on the traditional *** first investigate the permutation invariance of sequence data of the self-attention and apply it to point cloud *** we construct a voxel feature layer based on the self-attention to adaptively learn local and robust context of a voxel according to the spatial relationship and context information exchanging between all points within the ***,we construct a general voxel feature learning framework with the voxel feature layer as the core for 3D object *** voxel feature with Transformer(VFT)can be plugged into any other voxel-based 3D object detection framework easily,and serves as the backbone for voxel feature *** results on the KITTI dataset demonstrate that our method achieves the state-of-the-art performance on 3D object detection.

关键词： 3D object detection self-attention networks voxel feature with Transformer(VFT) point cloud encoder-decoder

来源：评论

学校读者我要写书评

暂无评论

Enhanced Long-Term Prediction Based on 2d Tensorization for Building Energy Consumption

SSRN

引用

SSRN 2023年

作者： Kong, Jun Deng, Song Jiang, Min Zhuang, Danfeng Jiangnan University Wuxi214122 China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China IBISC Univ. Evry Université Paris-Saclay 91020 France

Building energy consumption constitutes a significant contributor to greenhouse gas emissions. Accurate prediction of building energy consumption is crucial for building energy management, thus contributing to the reduction of green-house gas emissions. However, existing prediction algorithms often focus on within-period variations in energy con-sumption data and overlook between-period variations. This limitation makes it challenging to achieve precise predic-tion results, especially for long-term forecasts. To address this research gap, this paper proposes an Enhanced Long-Term Prediction based on 2D Tensorization (ELP2T) for building energy consumption. First, a Frequency-Guided 2D Tensorization Network (FG-2TN) is proposed. Energy consumption data, when represented as a one-dimensional time series, faces certain limitations, and FG-2TN is employed to address these limitations. Second, a Pro-gress-Optimized Deep Convolutional Network (PO-DCN) is proposed. It is designed to efficiently extract and learn features from the obtained two-dimensional tensors with fewer parameters and less time.. Third, A modular method is proposed to transform one-dimensional time series into two-dimensional tensors using Fast Fourier transform. These tensors are processed and then concatenated into a one-dimensional sequence for output. Final, a comparative analy-sis was conducted with various conventional prediction algorithms to demonstrate the superiority of the ELP2T model. The obtained average R² score for our proposed method is 0.807, representing an impressive enhancement of 11.06% compared to the most advanced alternatives. This substantial improvement firmly establishes the superiority of our approach. Especially, when considering a prediction length of 720 units, the performance gain of this metric increases to 18.09%, underscoring the pronounced advantage of our method in addressing long-term forecasting scenarios. © 2023, The Authors. All rights reserved.

关键词： Fast Fourier transforms

来源：评论

学校读者我要写书评

暂无评论

Multi-scale Frequency Enhancement Network for Blind Image Deblurring

arXiv

引用

arXiv 2024年

作者： Xiang, Yawen Zhou, Heng Li, Chengyang Li, Zhongbo Xie, Yongqiang The Institute of Systems Engineering Academy of Military Science Beijing100141 China School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China Beijing100100 China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China Beijing100100 China

Image deblurring is an essential image preprocessing technique, aiming to recover clear and detailed images form blurry ones. However, existing algorithms often fail to effectively integrate multi-scale feature extraction with frequency enhancement, limiting their ability to reconstruct fine textures. Additionally, non-uniform blur in images also restricts the effectiveness of image restoration. To address these issues, we propose a multi-scale frequency enhancement network (MFENet) for blind image deblurring. To capture the multi-scale spatial and channel information of blurred images, we introduce a multi-scale feature extraction module (MS-FE) based on depthwise separable convolutions, which provides rich target features for deblurring. We propose a frequency enhanced blur perception module (FEBP) that employs wavelet transforms to extract high-frequency details and utilizes multi-strip pooling to perceive non-uniform blur, combining multiscale information with frequency enhancement to improve the restoration of image texture details. Experimental results on the GoPro and HIDE datasets demonstrate that the proposed method achieves superior deblurring performance in both visual quality and objective evaluation metrics. Furthermore, in downstream object detection tasks, the proposed blind image deblurring algorithm significantly improves detection accuracy, further validating its effectiveness androbustness in the field of image deblurring. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：