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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Lu, Yunyao Wu, Yihang Kateb, Reem Chaddad, Ahmad College of Computer Science and Engineering Jeddah University Jeddah Saudi Arabia Aipm School of Artificial Intelligence Guilin University of Electronic Technology China École de Technologie Supérieure Laboratory for Imagery Vision and Artificial Intelligence Canada

ISBN: (纸本)9798331520526

Traditional supervised medical image segmentation models require large amounts of labeled data for training;however, obtaining such large-scale labeled datasets in the real world is extremely challenging. Recent semi-supervised segmentation models also suffer from noisy pseudo-label issue and limited supervision in feature space. To solve these challenges, we propose an innovative semi-supervised 3D medical image segmentation method to reduce the dependency on large, expert-labeled datasets. Furthermore, we introduce a dual-network architecture to address the limitations of existing methods in using contextual information and generating reliable pseudo-labels. In addition, a self-supervised contrastive learning strategy is used to enhance the representation of the network and reduce prediction uncertainty by distinguishing between reliable and unreliable predictions. Experiments on clinical magnetic resonance imaging demonstrate that our approach outperforms state-of-the-art techniques. Our code is available at https://***/AIPMLab/Semi-supervised-Segmentation. © 2025 IEEE.

关键词： contrastive learning Deep learning medical image segmentation semi -supervised learning

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Deep Modeling and Optimization of Medical Image Classification 22

Deep Modeling and Optimization of Medical Image Classificati...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Wu, Yihang Owais, Muhammad Kateb, Reem Chaddad, Ahmad College of Computer Science and Engineering Jeddah University Jeddah Saudi Arabia School of Artificial Intelligence Guilin University of Electronic Technology Aipm China Laboratory for Imagery Vision and Artificial Intelligence Ècole de Technologie Supérieure Canada

ISBN: (纸本)9798331520526

Deep models, such as convolutional neural networks (CNNs) and vision transformer (ViT), demonstrate remarkable performance in image classification. However, those deep models require large data to fine-tune, which is impractical in the medical domain due to the data privacy issue. Furthermore, despite the feasible performance of contrastive language image pre-training (CLIP) in the natural domain, the potential of CLIP has not been fully investigated in the medical field. To face these challenges, we considered three scenarios: 1) we introduce a novel CLIP variant using four CNNs and eight ViTs as image encoders for the classification of brain cancer and skin cancer, 2) we combine 12 deep models with two federated learning techniques to protect data privacy, and 3) we involve traditional machine learning (ML) methods to improve the generalization ability of those deep models in unseen domain data. The experimental results indicate that maxvit shows the highest averaged (AVG) test metrics (AVG = 87.03%) in HAM10000 dataset with multimodal learning, while convnext-1 demonstrates remarkable test with an F1-score of 83.98% compared to swin-b with 81.33% in FL model. Furthermore, the use of support vector machine (SVM) can improve the overall test metrics with AVG of 2% for swin transformer series in ISIC2018. Our codes are available at https://***/AIPMLab/SkinCancerSimulation. © 2025 IEEE.

关键词： Federated learning Foundation models Medical imaging

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Semantic and spatial‐spectral feature fusion transformer network for the classification of hyperspectral image

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1308-1322页

作者： Erxin Xie Na Chen Jiangtao Peng Weiwei Sun Qian Du Xinge You Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and StatisticsHubei UniversityWuhanChina Department of Geography and Spatial Information Techniques Ningbo UniversityNingboChina Department of Electrical and Computer Engineering Mississippi State UniversityMississippi StateMississippiUSA School of Electronic Information and Communications Huazhong University of Science and TechnologyWuhanChina

Recently,transformer‐based networks have been introduced for the classification of hyperspectral image(HSI).Although transformer‐based methods can well capture spectral sequence information,their ability to fuse different types of information contained in HSI is still *** exploit rich spectral,spatial and semantic information in HSI,a novel semantic and spatial‐spectral feature fusion transformer(S3FFT)network is proposed in this *** the proposed S3FFT method,spatial attention and efficient channel attention(ECA)modules are employed for the extraction of shallow spatialspectral ***,a transformer‐based module is designed to extract advanced fused features and to produce the pseudo‐label and class probability of each pixel for semantic feature ***,the semantic,spatial and spectral features are combined by the transformer for *** with traditional deep learning methods and recently transformer‐based methods,the proposed S3FFT shows relatively better results on three HSI datasets.

关键词： image classification machine learning

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ON THE INADEQUACY OF OPTIMIZING ALIGNMENT AND UNIFORMITY IN CONTRASTIVE LEARNING OF SENTENCE REPRESENTATIONS 11

ON THE INADEQUACY OF OPTIMIZING ALIGNMENT AND UNIFORMITY IN ...

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11th International Conference on Learning Representations, ICLR 2023

作者： Nie, Zhijie Zhang, Richong Mao, Yongyi SKLSDE Beihang University Beijing China Zhongguancun Laboratory Beijing China School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada Shen Yuan Honors College Beihang University Beijing China

Contrastive learning is widely used in areas such as visual representation learning (VRL) and sentence representation learning (SRL). Considering the differences between VRL and SRL in terms of negative sample size and evaluation focus, we believe that the solid findings obtained in VRL may not be entirely carried over to SRL. In this work, we consider the suitability of the decoupled form of contrastive loss, i.e., alignment and uniformity, in SRL. We find a performance gap between sentence representations obtained by jointly optimizing alignment and uniformity on the STS task and those obtained using contrastive loss. Further, we find that the joint optimization of alignment and uniformity during training is prone to overfitting, which does not occur on the contrastive loss. Analyzing them based on the variation of the gradient norms, we find that there is a property of "gradient dissipation" in contrastive loss and believe that it is the key to preventing overfitting. We simulate similar "gradient dissipation" of contrastive loss on four optimization objectives of two forms, and achieve the same or even better performance than contrastive loss on the STS tasks, confirming our hypothesis. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

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Filtering Balun Design using Hairpin-Ring Resonator and SSPP Metamaterial with Wide Notched band and Wide Stopband

Filtering Balun Design using Hairpin-Ring Resonator and SSPP...

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2023 IEEE MTT-S International Wireless Symposium, IWS 2023

作者： Qi, Huanhuan Liu, Haiwen Huang, Mo School of Information and Communications Engineering Xi' An Jiaotong University Xi'an China Faculty of Science and Technology State Key Laboratory of Analog and Mixed-Signal Vlsi China Institute of Microelectronics University of Macau Department of Electrical and Computer Engineering China

ISBN: (纸本)9798350336535

In this paper, a novel filtering balun based on microstrip-to-slotline transition is proposed. Utilizing the inherent out-of-phase electric field distributions on the adjacent grounds of slotline, the balun function is realized. In order to realize the wide notched-band and stopband performance for radio astronomical applications, spoof surface plasmon polariton (SSPP) metamaterial and multi-mode hairpin ring resonators (HRRs) are adopted. With these novel structures loaded, the proposed filtering balun exhibits a wide band from 1GHz to 6 GHz. The notched band is from 3.2 GHz to 4.4 GHz, within the notched band, the rejection level is better than 15 dB. Besides, the out-of-band suppression is below 35 dB in the wide frequency range from 6 GHz to 8 GHz. © 2023 IEEE.

关键词： Metamaterials

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Dependency-aware Task Offloading and Resource Pricing in Vehicular Edge Computing: A Stackelberg Game Approach

Dependency-aware Task Offloading and Resource Pricing in Veh...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Liang Zhao Shuai Huang Yuxiang Cao Huan Zhou Victor C. M. Leung Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering College of Computer and Information Technology China Three Gorges University Yichang China School of Computer Science Northwestern Polytechnical University Xi’an China Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Vehicular Edge Computing (VEC) allows vehicles to offload their delay-sensitive tasks to nearby Road Side Units (RSUs) for processing, which improves network quality of service (QoS). However, the self-interested SDN controller is unwilling to ask RSUs to provide free computing resources for vehicles. At the same time, complicated dependencies between vehicular subtasks may cause non-ideal task delay and energy consumption. In order to solve these problems, this paper proposes a Stackelberg game-based Dependency-aware task Offloading and resource Pricing framework (SDOP). Specifically, we first model a vehicular edge network that partially offloads dependency-aware tasks. Then, we depict the interaction between the SDN controller and vehicles as a Stackelberg game, with the goal of maximizing the utility of both parties. Next, we present a Gradient Ascent Plus Genetic algorithm (GAPG) to solve the problem. Finally, numerous simulations are performed, and the results show that compared with other baseline schemes, the proposed GAPG can significantly improve the utility of both the SDN controller and vehicles under various scenarios.

关键词： Energy consumption Computational modeling Road side unit Process control Games Pricing Quality of service Delays Edge computing Genetic algorithms

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APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semantic Segmentation

arXiv

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arXiv 2024年

作者： He, Weizhao Zhang, Yang Zhuo, Wei Shen, Linlin Yang, Jiaqi Deng, Songhe Sun, Liang Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China School of Computer Science University of Nottingham China

Few-shot semantic segmentation (FSS) endeavors to segment unseen classes with only a few labeled samples. Current FSS methods are commonly built on the assumption that their training and application scenarios share similar domains, and their performances degrade significantly while applied to a distinct domain. To this end, we propose to leverage the cutting-edge foundation model, the Segment Anything Model (SAM), for generalization enhancement. The SAM however performs unsatisfactorily on domains that are distinct from its training data, which primarily comprise natural scene images, and it does not support automatic segmentation of specific semantics due to its interactive prompting mechanism. In our work, we introduce APSeg, a novel auto-prompt network for cross-domain few-shot semantic segmentation (CD-FSS), which is designed to be auto-prompted for guiding cross-domain segmentation. Specifically, we propose a Dual Prototype Anchor Transformation (DPAT) module that fuses pseudo query prototypes extracted based on cycle-consistency with support prototypes, allowing features to be transformed into a more stable domain-agnostic space. Additionally, a Meta Prompt Generator (MPG) module is introduced to automatically generate prompt embeddings, eliminating the need for manual visual prompts. We build an efficient model which can be applied directly to target domains without fine-tuning. Extensive experiments on four cross-domain datasets show that our model outperforms the state-of-the-art CD-FSS method by 5.24% and 3.10% in average accuracy on 1-shot and 5-shot settings, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Game-Theoretic Dependent Task Offloading and Resource Pricing in Vehicular Edge Computing

Game-Theoretic Dependent Task Offloading and Resource Pricin...

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International Workshop on Quality of Service

作者： Liang Zhao Shuai Huang Huan Zhou Zilong Bai Victor C. M. Leung Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering College of Computer and Information Technology China Three Gorges University Yichang China School of Computer Science Northwestern Polytechnical University Xi’an China Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9798350350128

ISBN: (纸本)9798350350135

This paper proposes a Stacklberg game-based Dependent task Offloading and resource Pricing framework (SDOP), where vehicles partially offload their dependent substaks to the SDN controller and pays corresponding fees. Firstly, we model the interaction between the SDN controller and vehicles as a Stackelberg game, where both parties wish to maximize their utility. Then, we employ the backward induction approach to analyze the investigated problem, and prove the existence and uniqueness of Nash and Stackelberg equilibrium. Next, we propose a Gradient Ascent Plus Genetic algorithm (GAPG) to solve the considered problem. Finally, extensive simulation results show that the proposed GAPG outperforms other baseline schemes under various scenarios.

关键词： Simulation Computational modeling Pricing Games Quality of service Benchmark testing Optimization

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Unsupervised Image Region of Interest Detection using Discrete Cosine Transform 4

Unsupervised Image Region of Interest Detection using Discre...

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4th International Conference on Information Communication and Signal Processing, ICICSP 2021

作者： Olaode, Abass Naghdy, Golshah Utaminingrum, Fitri School of Electrical Computer and Telecommunication Engineering University of Wollongong Wollongong Australia Computer Vision Research Groups Faculty of Computer Science Brawijaya University Malang Indonesia

ISBN: (纸本)9781665407571

Spatial domain analysis of images has been recognised as an important task in image retrieval and computer vision, however, its application in the determination of image Region of Interests often require the inclusion of supervised training and similarity computations, both of which can be difficult to implement during the modelling of the images in a large number of images. however, unsupervised image Region of Interest determination via Frequency domain analysis can be implemented without training or similarity computation therefore it is more suitable for the modelling of images in a large set. This paper demonstrates image frequency domain analysis via Discrete Cosine Transform and presents its application in the unsupervised determination of Region of Interests for the elimination of the spatial incoherency often associated with the Bag-of-Visual Word Modelling. © 2021 IEEE.

关键词： Discrete cosine transforms

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Potential detection of Flavescence dorée in the vineyard using close-range hyperspectral imaging

Potential detection of Flavescence dorée in the vineyard us...

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2023 International Conference on electrical, computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Barjaktarovic, Marko Santoni, Massimo Faralli, Michele Bertamini, Massimo Bruzzone, Lorenzo School of Electrical Engineering University of Belgrade Serbia Remote Sensing Laboratory University of Trento Italy Remote Sensing Laboratory Department of Information Engineering and Computer Science University of Trento Italy University of Trento Italy Research and Innovation Centre Fondazione Edmund Mach Italy

ISBN: (纸本)9798350322972

Grapevine is one of the most important crops cultivated across Europe. Climate factors and diseases constantly threaten its production. Recently, Flavescence dorée (FD), an incurable grapevine disease with the obligation to uproot each infected plant, has been widely spread in Europe. The symptoms of FD are visually expressed in the late summer. Currently, the adopted procedure consists in scouting for infected plants by trained experts, which is time-consuming and not frequent enough. As stress development causes subtle spectral changes before any visible symptoms appear, during the summer of 2022, hyperspectral and multispectral images were acquired in the two vineyards near Riva del Garda, Trentino, Italy. A classification accuracy between 90.2 % and 96.9 % in distinguishing between infected and healthy plants was obtained from the hyperspectral data. These findings justify further efforts to use an in-house developed, affordable multispectral camera, significantly reducing equipment cost and procedure complexity while mapping the relevant spectral channels. © 2023 IEEE.

关键词： Hyperspectral imaging

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