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3rd International Conference on Geographic information and Remote Sensing Technology, GIRST 2024

作者： Zeng, Yuxian Zhang, Xinxin College of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology Xiamen361024 China

ISBN: (纸本)9781510689077

Deep learning techniques have significantly improved the accuracy and efficiency of change detection of very high resolution (VHR) images. However, many current models ignore the inherent heterogeneity of bi-temporal remote sensing images, thus making it difficult to distinguish between target changes and background variations. To address this problem, we propose a deep learning (DL) framework, namely temporal-spatial feature coordination network (TSFCN), to improve the accuracy of detection by enhancing the differentiation of change pixel features and improving the extraction and processing of change information. Further comparison and ablation experiments on the SVCD dataset show that the proposed model, compared with other models, improves the F1 scores and IoU by 2.09% and 3.81%, respectively. The results demonstrate that our model can achieve better accuracy and performance improvement. © 2025 SPIE.

关键词： Change detection

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GAN-DIRNet:A Novel Deformable image Registration Approach for Multimodal Histological images

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computers, Materials & Continua 2024年第7期80卷 487-506页

作者： Haiyue Li Jing Xie Jing Ke Ye Yuan Xiaoyong Pan Hongyi Xin Hongbin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of ChinaShanghai200240China Department of Pathology Ruijin HospitalShanghai Jiao Tong UniversitySchool of MedicineShanghai200025China Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai200240China Global Institute of Future Technology Shanghai Jiao Tong UniversityShanghai200240China

Multi-modal histological image registration tasks pose significant challenges due to tissue staining operations causing partial loss and folding of *** neural network(CNN)and generative adversarial network(GAN)are pivotal inmedical image ***,existing methods often struggle with severe interference and deformation,as seen in histological images of conditions like Cushing’s *** argue that the failure of current approaches lies in underutilizing the feature extraction capability of the discriminator *** this study,we propose a novel multi-modal registration approach GAN-DIRNet based on GAN for deformable histological image *** begin with,the discriminators of two GANs are embedded as a new dual parallel feature extraction module into the unsupervised registration networks,characterized by implicitly extracting feature descriptors of specific ***,modal feature description layers and registration layers collaborate in unsupervised optimization,facilitating faster convergence and more precise ***,experiments and evaluations were conducted on the registration of the Mixed National institute of Standards and Technology database(MNIST),eight publicly available datasets of histological sections and the Clustering-Registration-Classification-Segmentation(CRCS)dataset on the Cushing’s *** results demonstrate that our proposed GAN-DIRNet method surpasses existing approaches like DIRNet in terms of both registration accuracy and time efficiency,while also exhibiting robustness across different image types.

关键词： Histological images registration deformable registration generative adversarial network cushing’s disease machine learning computer vision

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Analysis of multimodal fusion strategies in deep learning for ischemic stroke lesion segmentation on computed tomography perfusion data

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Multimedia Tools and Applications 2025年第10期84卷 7493-7518页

作者： Raju, Chintha Sri Pothu Neelapu, Bala Chakravarthy Laskar, Rabul Hussain Muhammad, Ghulam Speech and Image Processing Lab Department of ECE National Institute of Technology Silchar Assam Silchar788010 India Department of Biotechnology and Medical Engineering National Institute of Technology Rourkela Odisha Rourkela769008 India Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia

Stroke poses a significant risk to human life. Segmenting and immediately treating the stroke core stops its further development, therefore, enhancing the likelihood of survival. Convolutional neural networks (CNN) have been very successful in medical image segmentation, namely in the field of deep learning, and have produced the most advanced outcomes. Multi-modal images provide superior outcomes in the segmentation of stroke lesions compared to single-modal images. The integration of input from several modalities at various levels is crucial in determining performance and producing diverse outcomes in deep learning models that use multimodalities. Further investigation is required to explore the optimal methods for processing multimodal data in CNNs, the influence of fusion on CNN learning, and the effect of fusion strategies on lesions of varying sizes. To examine the impact of a multi-modal fusion method on lesion segmentation, we assessed four models using distinct fusion techniques, including early, late, bottleneck, and hierarchical fusions. This study discusses the various fusion procedures used in segmenting the lesion using computed tomography perfusion data. In addition, both quantitative and qualitative assessments, including deep feature analysis and feature similarity, were conducted to assess the impact of the fusion technique on the model’s performance. Furthermore, we examined the influence of fusion techniques on the size of the lesion. In addition, we analyzed the advantages and disadvantages of several multi-modal fusion systems. Our findings demonstrate that the bottleneck fusion technique got the highest dice score, 0.582, on the Ischemic Stroke Lesion Segmentation 2018 validation data as a result of its capacity to construct complex relationships across several modalities. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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RBP-Former: Joint Prediction of RNA-protein Binding Sites on Full-length RNA Transcripts for Multiple RBPs

RBP-Former: Joint Prediction of RNA-protein Binding Sites on...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Li, Yichong Liu, Xiaojian Cheng, Fan Pan, Xiaoyong Yang, Yang Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai200240 China

ISBN: (纸本)9798350386226

RNA-binding proteins (RBPs) are essential for gene expression, and the complex RNA-protein interaction mechanisms require analysis of global RNA information. Therefore, accurate prediction of RBP binding sites on full-length RNA transcripts is crucial for understanding these mechanisms and their roles in diseases. While machine learning methods can predict RBP binding to RNA fragments, extending this to full-length transcripts presents challenges due to sequence length and data imbalance. In this paper, we introduce RBP-Former, a binding site joint prediction model designed specifically for full-length RNA transcripts that can be used for multiple RBPs. This model processes information at both coarse and fine-grained levels to fully exploit sequence data and its interactions with multiple RBPs. We develop multi-level imbalance learning strategies, achieving favorable results on imbalanced data. Our method outperforms existing methods in predicting binding sites on full-length RNA transcripts for multiple RBPs, demonstrating its effectiveness in handling imbalanced label and sample distributions. © 2024 IEEE.

关键词： Binding sites

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Achievement Degree Evaluation Method of Operating System Course Targets under the Background of engineering Education Accreditation 3

Achievement Degree Evaluation Method of Operating System Cou...

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3rd IEEE International Conference on computer Science and Educational Informatization, CSEI 2021

作者： Zhang, Fan Zhang, Xinhong Henan University School of Computer and Information Engineering Kaifeng China Institute of Image Processing and Pattern Recognition Henan University Kaifeng China

ISBN: (纸本)9781728184739

engineering education accreditation emphasizes the cultivation of students' ability to solve complex engineering problems, including the cultivation of students' ability to analyze, design and develop solutions, research, application of modern tools and other qualities related to non-technical factors to solve complex engineering problems. Operating system course is the core course of computer science and technology major in colleges and universities. The understanding and mastery of operating system course directly affects students' computer professional level. In view of the new requirements of new engineering construction and engineering education accreditation for major curriculum construction and personnel training, as well as the current characteristics of software engineering and personnel training, this paper, based on the core concept of OBE and complex engineering problems of engineering education accreditation, evaluates the achievement of operating system curriculum requirements under the background of engineering education accreditation. This paper analyzes the effectiveness and puts forward a new method of major curriculum construction and evaluation to meet the requirements of new engineering construction and engineering education accreditation. © 2021 IEEE.

关键词： engineering education

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A Novel Wideband CPW-Fed 5.8GHz RFID Tag Antenna

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Chinese Journal of Electronics 2023年第2期21卷 202-208页

作者： Huihui Li Xuanqin Mou Zhen Ji Hang Yu Yan Li Institute of Image Processing and Pattern Recognition Xi'an Jiaotong University Xi'an China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Key Laboratory of Embedded System Design Shenzhen China

A novel wideband 5.8GHz CPW-fed antenna is presented for Radio frequency identification (RFID) tag. Four U-shaped and four L-shaped branches are used as additional resonators to achieve wideband operation. The proposed antenna was analyzed numerically using the Method of moment (MOM) and the Finite element method (FEM). With the antenna size limited to $30\times 30 \text{mm}^{2}$ , the −10dB bandwidth obtained by MOM is 3.235GHz (5.765∼9GHz) and the −9.5dB band-width obtained by FEM is 2.74GHz (5.32∼8.06GHz), corresponding to 55.7% and 47.2% of the center frequency 5.8GHz respectively. Moreover, the simulated results show that the proposed antenna has gain of more than 4.8dBi and the radiation pattern is nearly omnidirectional in the H-plane. The measured −10dB bandwidth is 2.68GHz (5.63GHz∼8.31GHz), 46.2% of the 5.8GHz frequency. Furthermore, there are three measured resonant frequencies at 1.34GHz, 3.23GHz and 5.8GHz with lower than −10dB return loss respectively. The measurement result achieves a wideband RFID tag antenna performance and is in good agreement with the calculated results.

关键词： Antenna measurements Resonant frequency Loss measurement Frequency measurement Finite element analysis Broadband antennas RFID tags

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Isoform Function Prediction Based on Heterogeneous Graph Attention Networks

Isoform Function Prediction Based on Heterogeneous Graph Att...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Guo, Kuo Li, Yifan Chen, Hao Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Institute of Image Processing and Pattern Recognition Shanghai200240 China Carnegie Mellon University School of Computer Science Computational Biology Department PittsburghPA15213 United States

ISBN: (纸本)9798350337488

Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bioinformatics as it can provide valuable insights into the intricate mechanisms of gene regulation and biological processes. Conventionally, gene function labels are standardized in Gene Ontology (GO) terms. However, traditional methods for predicting isoform function are largely limited by the absence of isoform-specific labels, sparse annotations, and the vast number of GO terms. To address these issues, we propose HANIso, a deep learning-based method for isoform function prediction. HANIso leverages a pretrained protein language model to extract features from protein sequences. It also integrates heterogeneous information, such as isoform sequence features, GO annotations, and isoform interaction data, using a Heterogeneous Graph Attention Network (HAN). This allows the model to learn the importance of different sources of information and their semantic relationships through the attention mechanism. Our method can predict function labels at both the gene level and isoform level. We conduct experiments on two species datasets, and the results demonstrate that our method outperforms existing methods on both AUROC and AUPRC. HANIso has the potential to overcome the limitations of traditional methods and provide a more accurate and comprehensive understanding of isoform function. © 2023 IEEE.

关键词： alternative splicing gene ontology heterogeneous graph attention network isoform function prediction protein language model

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SliceProp: A Slice-Wise Bidirectional Propagation Model for Interactive 3D Medical image Segmentation 1

SliceProp: A Slice-Wise Bidirectional Propagation Model for ...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Xu, Xin Lu, Wenjing Lei, Jiahao Qiu, Peng Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine Department of Vascular Surgery China Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (纸本)9798350358780

Interactive medical image segmentation methods have become increasingly popular in recent years. These methods combine manual labeling and automatic segmentation, reducing the workload of annotation while maintaining high accuracy. However, most current interactive segmentation frameworks are limited to 2D image data, and are not suitable for 3D image data due to the large size and high complexity of 3D data, as well as the challenges posed by information asymmetry and sparse annotation. In this paper, we propose SliceProp, an interactive segmentation framework that implements slice-wise Label Bidirectional Propagation (LBP) for 3D medical image segmentation. SliceProp extends the interactive 2D image segmentation algorithm to 3D image segmentation, and can handle 3D data with large size and high complexity. Moreover, equipped with a Backtracking Feedback Check (BFC) module, SliceProp effectively addresses the issues of information asymmetry and spatial sparse annotation in 3D medical image segmentation. Additionally, we adopt an uncertainty-based criterion to pri-oritize the slices to be refined interactively, which enhances the efficiency of the interaction process by enabling the model to focus on the regions with the most unreliable predictions. SliceProp is evaluated on two datasets and achieves promising results compared to state-of-the-art methods. © 2023 IEEE.

关键词： Medical imaging

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Deep Learning in Palmprint recognition-A Comprehensive Survey

arXiv

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arXiv 2025年

作者： Gao, Chengrui Yang, Ziyuan Jia, Wei Leng, Lu Zhang, Bob Teoh, Andrew Beng Jin College of Computer Science Sichuan University Chengdu610065 China Singapore School of Computer and Information Hefei University of Technology Hefei China Jiangxi Provincial Key Laboratory of Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China Pattern Analysis and Machine Intelligence Group Department of Computer and Information Science University of Macau Taipa China School of Electrical and Electronic Engineering College of Engineering Yonsei University Seoul Korea Republic of

Palmprint recognition has emerged as a prominent biometric technology, widely applied in diverse scenarios. Traditional handcrafted methods for palmprint recognition often fall short in representation capability, as they heavily depend on researchers' prior knowledge. Deep learning (DL) has been introduced to address this limitation, leveraging its remarkable successes across various domains. While existing surveys focus narrowly on specific tasks within palmprint recognition-often grounded in traditional methodologies-there remains a significant gap in comprehensive research exploring DL-based approaches across all facets of palmprint recognition. This paper bridges that gap by thoroughly reviewing recent advancements in DL-powered palmprint recognition. The paper systematically examines progress across key tasks, including region-of-interest segmentation, feature extraction, and security/privacy-oriented challenges. Beyond highlighting these advancements, the paper identifies current challenges and uncovers promising opportunities for future research. By consolidating state-of-the-art progress, this review serves as a valuable resource for researchers, enabling them to stay abreast of cutting-edge technologies and drive innovation in palmprint recognition. Copyright © 2025, The Authors. All rights reserved.

关键词： Biometrics

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MBD-Net: Multi-Branch Dilated Convolutional Network With Cyst Discriminator for Renal Multi-Structure Segmentation

MBD-Net: Multi-Branch Dilated Convolutional Network With Cys...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Yusheng Liu Yingjie Zhao Meihuan Wang Yichao Hao Xiuying Wang Lisheng Wang Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China College of Medicine and Biological Information Engineering Northeastern University Shenyang China School of Computer Science The University of Sydney Sydney NSW Australia

In surgery-based renal cancer treatment, one of the most essential tasks is the three-dimensional (3D) kidney parsing on computed tomography angiography (CTA) images. In this paper, we propose an end-to-end convolutional neural network-based framework to segment multiple renal structures, including kidneys, kidney tumors, arteries, and veins from arterial-phase CT images. Our method consists of two collaborative modules: First, we propose an encoding-decoding network, named Multi-Branch Dilated Convolutional Network (MBD-Net), consisting of residual, hybrid dilated convolutional, and reduced-dimensional convolutional structures, which improves the feature extraction ability with relatively fewer network parameters. Given that renal tumors and cysts have confusing geometric structures, we also design the Cyst Discriminator to effectively distinguish tumors from cysts without labeling information via gray-scale curves and radiographic features. We have quantitatively evaluated our approach on a publicly available dataset from MICCAI 2022 Kidney Parsing for Renal Cancer Treatment Challenge (KiPA2022), with mean Dice similarity coefficient (DSC) as 96.18%, 90.99%, 88.66% and 80.35% for the kidneys, kidney tumors, arteries, and veins respectively, winning the stable and top performance in the *** relevance—The proposed CNN-Based framework can automatically segment 3D kidneys, renal tumors, arteries, and veins for kidney parsing techniques, benefiting surgery-based renal cancer treatment.

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