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MAINet: Modality-Aware Interaction Network for Medical Image Fusion

ACM Transactions on Multimedia Computing, Communications, and Applications 1000年

作者： Lisi Wei Libo Zhao Xiaoli Zhang College of Computer Science and Technology Jilin University China College of Artificial Intelligence and Big Data Hulunbuir University China and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China College of Computer Science and Technology Jilin University China and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China

Due to the limitations of imaging sensors, obtaining a medical image that simultaneously captures both functional metabolic data and structural tissue details remains a significant challenge in clinical diagnosis. To address this, Multimodal Medical Image Fusion (MMIF) has emerged as an effective technique for integrating complementary information from multimodal source images, such as CT, PET, and SPECT, which is critical for providing a comprehensive understanding of both anatomical and functional aspects of the human body. One of the key challenges in MMIF is how to exchange and aggregate this multimodal information. This paper rethinks MMIF by addressing the harmony of modality gaps and proposes a novel Modality-Aware Interaction Network (MAINet), which leverages cross-modal feature interaction and progressively fuses multiple features in graph space. Specifically, we introduce two key modules: the Cascade Modality Interaction (CMI) module and the Dual-Graph Learning (DGL) module. The CMI module, integrated within a multi-scale encoder with triple branches, facilitates complementary multimodal feature learning and provides beneficial feedback to enhance discriminative feature learning across modalities. In the decoding process, the DGL module aggregates hierarchical features in two distinct graph spaces, enabling global feature interactions. Moreover, the DGL module incorporates a bottom-up guidance mechanism, where deeper semantic features guide the learning of shallower detail features, thus improving the fusion process by enhancing both scale diversity and modality awareness for visual fidelity results. Experimental results on medical image datasets demonstrate the superiority of the proposed method over existing fusion approaches in both subjective and objective evaluations. We also validated the performance of the proposed method in applications such as infrared-visible image fusion and medical image segmentation.

关键词： Multimodal fusion Medical image fusion Cascade modality interaction Modality-awareness Graph convolutional network

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Improving Generalization of Neural Vehicle Routing Problem Solvers Through the Lens of Model Architecture

arXiv

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arXiv 2024年

作者： Xiao, Yubin Wang, Di Wu, Xuan Wu, Yuesong Li, Boyang Du, Wei Wang, Liupu Zhou, You Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun130012 China Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly Nanyang Technological University 639956 Singapore WeBank-NTU Joint Research Institute on Fintech Nanyang Technological University 639956 Singapore College of Computing and Data Science Nanyang Technological University 639956 Singapore

Neural models produce promising results when solving Vehicle Routing Problems (VRPs), but may often fall short in generalization. Recent attempts to enhance model generalization often incur unnecessarily large training cost or cannot be directly applied to other models solving different VRP variants. To address these issues, we take a novel perspective on model architecture in this study. Specifically, we propose a plug-and-play Entropy-based Scaling Factor (ESF) and a Distribution-Specific (DS) decoder to enhance the size and distribution generalization, respectively. ESF adjusts the attention weight pattern of the model towards familiar ones discovered during training when solving VRPs of varying sizes. The DS decoder explicitly models VRPs of multiple training distribution patterns through multiple auxiliary light decoders, expanding the model representation space to encompass a broader range of distributional scenarios. We conduct extensive experiments on both synthetic and widely recognized real-world benchmarking datasets and compare the performance with seven baseline models. The results demonstrate the effectiveness of using ESF and DS decoder to obtain a more generalizable model and showcase their applicability to solve different VRP variants, i.e., travelling salesman problem and capacitated VRP. Notably, our proposed generic components require minimal computational resources, and can be effortlessly integrated into conventional generalization strategies to further elevate model generalization. Copyright © 2024, The Authors. All rights reserved.

关键词： Combinatorial optimization

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A hybrid discrete PSO-SA algorithm to find optimal elimination orderings for Bayesian networks

A hybrid discrete PSO-SA algorithm to find optimal eliminati...

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International Conference on Industrial and Information Systems, IIS

作者： Xuchu Dong Dantong Ouyang Dianbo Cai Yonggang Zhang Yuxin Ye Department of Computer Science and Technology Jilin University Changchun Jilin China Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Changchun Jilin China Network Monitor Maintenance Centre China Telecom Corporation Limited Changchun Jilin China

In this paper, a hybrid algorithm named DPSOSA is proposed to find near-to-optimal elimination orderings in Bayesian networks. DPSO-SA is a discrete particle swarm optimization method enhanced by simulated annealing. ... 详细信息

关键词： computational modeling Adaptation model

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An adaptive multi-heuristic ant colony system for finding optimal elimination orderings in Bayesian networks

An adaptive multi-heuristic ant colony system for finding op...

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International Conference on Electrical engineering, Computing Science and Automatic Control (CCE)

作者： Xuchu Dong Yonggang Zhang Dianbo Cai Haihong Yu Yuxin Ye Department of Computer Science and Technology Jilin University Changchun Jilin China Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Changchun Jilin China Network Monitor Maintenance Centre China Telecom Corporation Limited Changchun Jilin China

To find an optimal elimination ordering for Bayesian networks, a multi-heuristic-based ant colony system named MHC-HS-ACS is proposed. MHC-HS-ACS uses a set of heuristics to guide the ants to search solutions. The heuristic set can evolve with the searching procedure in an adaptive way. MHC-HS-ACS also utilizes a heuristic-based local search to accelerate its convergence. computational experiments show that MHC-HS-ACS can find very high quality solutions.

关键词： Bayesian methods Ethics Junctions Conferences Electrical engineering IEEE catalog Inference algorithms

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JC5A: Service Delay Minimization for Aerial MEC-assisted Industrial Cyber-Physical Systems

arXiv

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arXiv 2024年

作者： Sun, Geng Wu, Jiaxu Sun, Zemin He, Long Wang, Jiacheng Niyato, Dusit Jamalipour, Abbas Mao, Shiwen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Electrical and Computer Engineering The University of Sydney SydneyNSW2006 Australia Department of Electrical and Computer Engineering Auburn University Auburn United States

In the era of the sixth generation (6G) and industrial Internet of Things (IIoT), an industrial cyber-physical system (ICPS) drives the proliferation of sensor devices and computing-intensive tasks. To address the limited resources of IIoT sensor devices, unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has emerged as a promising solution, providing flexible and cost-effective services in close proximity of IIoT sensor devices (ISDs). However, leveraging aerial MEC to meet the delay-sensitive and computation-intensive requirements of the ISDs could face several challenges, including the limited communication, computation and caching (3C) resources, stringent offloading requirements for 3C services, and constrained on-board energy of UAVs. To address these issues, we first present a collaborative aerial MEC-assisted ICPS architecture by incorporating the computing capabilities of the macro base station (MBS) and UAVs. We then formulate a service delay minimization optimization problem (SDMOP). Since the SDMOP is proved to be an NP-hard problem, we propose a joint computation offloading, caching, communication resource allocation, computation resource allocation, and UAV trajectory control approach (JC5A). Specifically, JC5A consists of a block successive upper bound minimization method of multipliers (BSUMM) for computation offloading and service caching, a convex optimization-based method for communication and computation resource allocation, and a successive convex approximation (SCA)-based method for UAV trajectory control. Moreover, we theoretically prove the convergence and polynomial complexity of JC5A. Simulation results demonstrate that the proposed approach can achieve superior system performance compared to the benchmark approaches and algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Convex optimization

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Cooperative coevolutionary genetic algorithms to find optimal elimination orderings for Bayesian networks

Cooperative coevolutionary genetic algorithms to find optima...

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International Conference on Bio-Inspired Computing: Theories and Applications, BICTA

作者： Xuchu Dong HaiHong Yu Dantong Ouyang Dianbo Cai Yuxin Ye Yonggang Zhang Department of Computer Science and Technology Jilin University Changchun Jilin China Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Changchun Jilin China Network Monitor Maintenance Centre China Telecom Corporation Limited Changchun Jilin China

According to the characteristics of the optimal elimination ordering problem in Bayesian networks, a heuristic-based genetic algorithm, a cooperative coevolutionary genetic framework and five grouping schemes are proposed. Based on these works, six cooperative coevolutionary genetic algorithms are constructed. Numerical experiments show that these algorithms are more robust than other existing swarm intelligence methods when solving the elimination ordering problem.

关键词： Genetics Bayesian methods Robustness

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Generative AI for Deep Reinforcement Learning: Framework, Analysis, and Use Cases

arXiv

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arXiv 2024年

作者： Sun, Geng Xie, Wenwen Niyato, Dusit Mei, Fang Kang, Jiawen Du, Hongyang Mao, Shiwen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Automation Guangdong University of Technology Guangzhou510006 China Department of Electrical and Electronic Engineering The University of Hong Kong 999077 Hong Kong Department of Electrical and Computer Engineering Auburn University Auburn36830 United States

As a form of artificial intelligence (AI) technology based on interactive learning, deep reinforcement learning (DRL) has been widely applied across various fields and has achieved remarkable accomplishments. However, DRL faces certain limitations, including low sample efficiency and poor generalization. Therefore, we present how to leverage generative AI (GAI) to address these issues above and enhance the performance of DRL algorithms in this paper. We first introduce several classic GAI and DRL algorithms and demonstrate the applications of GAI-enhanced DRL algorithms. Then, we discuss how to use GAI to improve DRL algorithms from the data and policy perspectives. Subsequently, we introduce a framework that demonstrates an actual and novel integration of GAI with DRL, i.e., GAI-enhanced DRL. Additionally, we provide a case study of the framework on UAV-assisted integrated near-field/far-field communication to validate the performance of the proposed framework. Moreover, we present several future directions. Finally, the related code is available at: https://***/GAI-enhanced-DRL. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Novel trusted authentication and evaluation framework in VANET

引用

Tongxin Xuebao/Journal on Communications 2009年第1 A期30卷 107-113页

作者： Wu, Jing Liu, Yan-Heng Wang, Jian Li, Wei-Ping College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China Changchun Jilin University Information Technologies Co. Ltd. Changchun 130012 China

Vehicular ad hoc networks (VANET) have many new characteristics unlike tranditional network, such as centriclessness, mobility and multi-hop transmission, which invalidate conventional key managements. The fast development and successful application of trusted computing technology provide a potential solution to achieve effective authentication and trust evaluation. According to the VANET with self-organization, a new authentication and trust evaluation framework was proposed based on trusted computing technology, the concepts of "trusted attribute" and "trusted grade" were introduced, and the key management architecture as well as authentication process of mobile node were explained in detail. Moreover, the potential real application of the presented framework was discussed, and the trust evaluation method and its argumentation in the presented framework were given.

关键词： Authentication

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Elucidating Spatial Complex Structures from Mass Spectrometry Imaging with Deep Multimodal Model

Elucidating Spatial Complex Structures from Mass Spectrometr...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Jiangnan Cui Junjie Xia Xuan Li Yan Wang Fufang Qiu Yaochen Xu Keren Xu Chunman Zuo School of Computer Science and Technology Institute of Artificial Intelligence Donghua University Shanghai China Institute of Artificial Intelligence Donghua University Shanghai China College of Computer Science and Technology Jilin University Changchun China Department of Neurosurgery Huashan Hospital Shanghai China CAS Key Laboratory of Systems Biology Shanghai Institute of Biochemistry and Cell Biology Center for Excellence in Molecular Cell Science Chinese Academy of Sciences Shanghai China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

Spatial mass spectrometry imaging (SMSI) technology has enabled the characterization of biomolecule patterns within the tissue microenvironment, yet its analyses suffer from substantial noise. The lack of effective methods for exploiting multi-view features (e.g., spatial location) has hindered their elucidation ability for tissue heterogeneity. Here, we propose MSG, a multimodal modal that integrates histology, spatial location, and MSI data, in deciphering tissue structure by graph neural network. Specifically, MSG learns low-dimensional representations by aggregating histomorphological features from neighboring tiles via graph attention autoencoder. MSG outperforms other methods in identifying spatial domains and denoising data. The subsequent domain-guided differential expression analysis of the denoised data enables the identification of ions with significantly enriched patterns within the recognized domains. MSG exhibits its versatility in handling SMSI data from various platforms.

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Distilling Autoregressive Models to Obtain High-Performance Non-Autoregressive Solvers for Vehicle Routing Problems with Faster Inference Speed

arXiv

引用

arXiv 2023年

作者： Xiao, Yubin Wang, Di Li, Boyang Wang, Mingzhao Wu, Xuan Zhou, Changliang Zhou, You Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University China Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly Nanyang Technological University Singapore WeBank-NTU Joint Research Institute on Fintech Nanyang Technological University Singapore School of Computer Science and Engineering Nanyang Technological University Singapore School of System Design and Intelligent Manufacturing Southern University of Science and Technology China

Neural construction models have shown promising performance for Vehicle Routing Problems (VRPs) by adopting either the Autoregressive (AR) or Non-Autoregressive (NAR) learning approach. While AR models produce high-quality solutions, they generally have a high inference latency due to their sequential generation nature. Conversely, NAR models generate solutions in parallel with a low inference latency but generally exhibit inferior performance. In this paper, we propose a generic Guided Non-Autoregressive knowledge Distillation (GNARKD) method to obtain high-performance NAR models having a low inference latency. GNARKD removes the constraint of sequential generation in AR models while preserving the learned pivotal components in the network architecture to obtain the corresponding NAR models through knowledge distillation. We evaluate GNARKD by applying it to three widely adopted AR models to obtain NAR VRP solvers for both synthesized and real-world instances. The experimental results demonstrate that GNARKD significantly reduces the inference time (4∼5× faster) with acceptable performance drop (2∼3%). To the best of our knowledge, this study is first-of-its-kind to obtain NAR VRP solvers from AR ones through knowledge distillation. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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