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A Multi-scale Framework for Out-of-Distribution Detection in Dermoscopic Images 1

4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Huang, Zhongzheng Wang, Tao Cai, Yuanzheng Liang, Lingyu College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering and International Digital Economy College Minjiang University Fuzhou350108 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan354300 China Fujian Yilian-Health Nursing Information Technology Co. Ltd. Fuzhou350003 China School of Electronic and Information Engineering South China University of Technology Guangzhou510641 China

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

The automatic detection of skin diseases via dermoscopic images can improve the efficiency in diagnosis and help doctors make more accurate judgments. However, conventional skin disease recognition systems may produce high confidence for out-of-distribution (OOD) data, which may become a major security vulnerability in practical applications. In this paper, we propose a multi-scale detection framework to detect out-of-distribution skin disease image data to ensure the robustness of the system. Our framework extracts features from different layers of the neural network. In the early layers, rectified activation is used to make the output features closer to the well-behaved distribution, and then an one-class SVM is trained to detect OOD data;in the penultimate layer, an adapted Gram matrix is used to calculate the features after rectified activation, and finally the layer with the best performance is chosen to compute a normality score. Experiments show that the proposed framework achieves superior performance when compared with other state-of-the-art methods in the task of skin disease recognition. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Chemical activation

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STMAD:A Simple Multimodal Anomaly Detection for Thyroid Medical information 17

STMAD:A Simple Multimodal Anomaly Detection for Thyroid Medi...

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17th International Congress on Image and Signal processing, BioMedical Engineering and Informatics, CISP-BMEI 2024

作者： Xu, Wentong Fu, Chao Yu, Jing Si, Caifeng Li, Zuoyong Li, Chaochao Fan, Haoyi School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China First Affiliated Hospital of Zhengzhou University Department of Ultrasound Henan Zhengzhou450052 China School of Computer and Big Data Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou350121 China

ISBN: (纸本)9798331507398

Anomaly detection has been widely explored by training an out-of-distribution detector with only normal data. Medical image analysis, as a common tool, faces the challenge of how to further enhance the detection performance for thyroid cancer, especially with the application of various deep learning methods in the thyroid-related field. In this paper, we validate the effectiveness of sonographic feature descriptions (textual information) for thyroid cancer diagnosis. Building on this, we propose a simple multimodal self-supervised framework that leverages both textual and image modalities for thyroid cancer anomaly detection. Results on a private dataset demonstrate the effectiveness and superiority of our proposed method compared to advanced methods. © 2024 IEEE.

关键词： Self-supervised learning

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On the Impact of Spatial Mode Dispersion for Strongly-Coupled Multicore Fiber Submarine Transmission

On the Impact of Spatial Mode Dispersion for Strongly-Couple...

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2023 Optical Fiber Communications Conference and Exhibition, OFC 2023

作者： Sun, Lin Chen, Bin Liu, Gordon Ning Cai, Yi Li, Zhaohui Lu, Chao Shen, Gangxiang Soochow University Suzhou Key Laboratory of Advanced Optical Communication Network Technology School of Electronic and Information Engineering Suzhou215006 China Hefei University of Technology School of Computer Science and Information Engineering Hefei China Sun Yat-Sen University Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips School of Electronics and Information Technology Guangzhou511400 China The Hong Kong Polytechnic University Photonics Research Center Department of Electrical and Information Engineering Kowloon Hung Hom Hong Kong

Strongly-coupled multicore fibers exhibit the improved tolerance to fiber non-linearity. Their potentials in optical submarine communications are investigated with considering the coupling length and spatial mode disp... 详细信息

ISBN: (纸本)9781957171180

关键词： Submarines

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Stable Learning via Triplex Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 5267-5276页

作者： Yang, Shuai Jiang, Tingting Dang, Qianlong Gu, Lichuan Wu, Xindong Anhui Agricultural University School of Information and Artificial Intelligence Hefei230036 China Anhui Provincial Engineering Research Center for Agricultural Information Perception and Intelligent Computing Hefei230036 China Northwest A & F University College of Science Yangling712100 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei230601 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China

Stable learning aims to learn a model that generalizes well to arbitrary unseen target domain by leveraging a single source domain. Recent advances in stable learning have focused on balancing the distribution of confounders for each feature to eliminate spurious correlations. However, previous studies treat all features equally without considering the difficulties of confounder balancing associated with different features, and regard irrelevant features as confounders, deteriorating generalization performance. To tackle these issues, this article proposes a novel triplex learning (TriL) based stable learning algorithm, which performs sample reweighting, causal feature selection, and representation learning to remove spurious correlations. Specifically, first, TriL adaptively assigns weights to the confounder balancing term of each feature in accordance with the difficulties of confounder balancing, and aligns the confounder distribution of each feature by learning a group of sample weights. Second, TriL integrates the sample weights into a weighted cross-entropy model to compute causal effects of features for excluding irrelevant features from the confounder set. Finally, TriL relearns a set of sample weights and uses them to guide a new supervised dual-autoencoder containing two classifiers to learn feature representations. TriL forces the results of two classifiers to remain consistent for removing spurious correlations by using a cross-classifier consistency regularization. Extensive experiments on synthetic and two real-world datasets show the superiority of TriL compared with seven methods. © 2024 IEEE.

关键词： Feature Selection

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UAV-Aided Localization and Communication: Joint Frame Structure, Beamwidth, and Power Allocation

IEEE Journal of Selected Areas in Sensors

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IEEE Journal of Selected Areas in Sensors 2024年 1卷 154-165页

作者： Liang, Tianhao Zhang, Tingting Zhou, Sheng Liu, Wentao Li, Dong Zhang, Qinyu School of Electronics and Information Engineering Shenzhen518055 China Guangdong Provincial Key Laboratory of Space-Aerial Networking and Intelligent Sensing 518055 China Pengcheng Laboratory Shenzhen150001 China Tsinghua University Department of Electronic Engineering Beijing100084 China Macau University of Science and Technology School of Computer Science and Engineering 999078 China

In wireless sensors networks, integrating localization and communication technique is crucial for efficient spectrum and hardware utilizations. In this article, we present a novel framework of the unmanned aerial vehicle (UAV)-aided localization and communication for ground node (GN), where the average spectral efficiency (SE) is used to reveal the intricate relationship among the frame structure, channel estimation error, and localization accuracy. In particular, we first derive the lower bounds for channel estimation error and the 3-D location prediction error, respectively. Leveraging these comprehensive analysis, we formulate a problem to maximize the average SE in the UAV-GN communication, where the frame structure, beamwidth, and power allocation can be jointly optimized. Subsequently, we propose an efficient iterative algorithm to address this nonconvex problem with closed-form expressions for beamwidth design and power allocation. Numerical results demonstrate that the performance of our proposed method can approach the upper bound with low complexity, and achieve over 70% performance gain compared with communication-only benchmarks. In addition, the analysis highlights the dominated impacts of the Doppler effect on the average SE. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Geometry Dependent Multi-stage Compact Thermal Model for Advanced Nanosheet FETs

Geometry Dependent Multi-stage Compact Thermal Model for Adv...

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2023 International Symposium of Electronics Design Automation, ISEDA 2023

作者： Chen, Haifeng Chen, Wangyong Chen, Jiahui Cai, Linlin School of Microelectronics Science and Technology Sun Yat-sen University Guangzhou510275 China Sun Vatsen University Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Guangzhou510275 China

ISBN: (纸本)9798350304510

Gate-All-Around Nanosheet FETs (NSFETs) are promising candidates for sub 5nm node, however, the aggressive scaling NSFETs are susceptible to the self-heating effect (SHE). A precise thermal model compatible with industry SPICE model, e.g. BSIMCMG, is critical to improve the yield of NSFET-based integrated circuit design and to reduce the development timeline of the advanced process. In this paper, we propose a geometry dependent multi-stage thermal RC network to accurately predict self-heating over a wide frequency range, from DC to THz, for the advanced NSFETs, which is validated by the transient thermal simulations based on 3-D Finite Element Modeling (FEM) method. The proposed compact thermal model enables to take the impacts of nanosheet width, number of stacks (gate fingers) and gate length into account, where the stage and model parameters of the thermal RC model are determined by Bayesian deconvolution and Particle Swarm Optimization (PSO) methods, respectively. The underlying physical understandings behind the geometry dependent thermal behaviors are discussed in detail. © 2023 IEEE.

关键词： Nanosheets

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GA-Net: Gated Attention Mechanism Based Global Refinement Network for Image Inpainting 29

GA-Net: Gated Attention Mechanism Based Global Refinement Ne...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Qiu, Ran Zhao, Shengrong Liang, Hu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

Underwater images are often affected by problems such as light attenuation, color distortion, noise and scattering, resulting in image defects. A novel image inpainting method is proposed to intelligently predict and fill damaged areas for complete and continuous visualization of the image. First, in order to effectively solve the problem of color distortion caused by light refraction in underwater environments, the improved gated attention mechanism is used. This mechanism improves the local details by learning and weighting the important features of the image. Second, gated convolution automatically determines the degree of restoration for each pixel based on local features of the original image. It eliminates distractions such as low contrast and scattering, retaining more original detailed information. By doing so, image inpainting techniques improve the quality and visualization of underwater images. © 2023 IEEE.

关键词： Gated convolution Image inpainting Underwater images

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Integrated Optical Communication and Distributed Sensing Systems

Integrated Optical Communication and Distributed Sensing Sys...

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2024 Photonic Networks and Devices, NETWORKS 2024

作者： Lu, Chao Wang, Jingchuang Chen, Shaoyi Yan, Yaxi Li, Tianrui Lu, Liwang Wu, Yichang Huang, Wenjin Lau, Alan Pak Tao Li, Zhaohui Photonic Research Institute Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems School of Electronic and Information Technology Sun Yat-Sen University Guangzhou519082 China

Recent work on integrating sensing functions into optical communication systems is described. The developed techniques are expected to enhance the reliability of optical networks and enable a range of applications in ... 详细信息

关键词： Marine applications

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A Near Lossless Learned Image Coding Network Quantization Approach for Cross-Platform Inference

A Near Lossless Learned Image Coding Network Quantization Ap...

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2023 IEEE International Conference on Visual Communications and Image processing, VCIP 2023

作者： Hang, Xinyu Jia, Chuanmin Ma, Siwei Gao, Wen Peking University National Engineering Research Center on Visual Technology School of Computer Science China Peking University National Key Laboratory for Multimedia Information Processing School of Computer Science China Peking University Wangxuan Institute of Computer Technology China

ISBN: (纸本)9798350359855

This paper presents an innovative pipeline design for the quantization of neural image codecs, ensuring robust and consistent deployment across various platform inferences. Our approach addresses a significant challenge prevalent in neural image compression networks: the lack of quantization methods for entropy probability hyper-priors, such as Gaussian mixture models (GMMs), in the existing literature. Our method serves as a solution to prevent decoding errors stemming from float-point discrepancies between the encoder and decoder, especially when executed on different platforms. By implementing a quantization scheme based on look-up tables (LUTs), we achieve precise quantization of GMM probability models with minimal performance degradation, approaching near losslessness. Moreover, our contribution offers a comprehensive quantization pipeline that facilitates dependable data transmission and harmonious synchronization of arithmetic coding algorithms between the encoder and decoder components. Empirical evidence highlights the effectiveness of our method, demonstrating seamless codec matching on diverse platforms and substantiating its capacity to enhance the integrity and security of compressed data. This work enhances the practical applicability of neural compression techniques across real-world scenarios. © 2023 IEEE.

关键词： Table lookup

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Graph Matching Based Graph Self-Supervised Learning for Molecular Property Prediction

Graph Matching Based Graph Self-Supervised Learning for Mole...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lin, Hongxiang Zhou, Yixiao Hu, Huiying He, Zhicheng Wu, Runzhi Lyu, Xiaoqing Beijing Jiaotong University Weihai China Tianjin University Tianjin China Wangxuan Institute of Computer Technology Peking University Beijing China State Key Laboratory of Multimedia Information Processing Peking University Beijing China

ISBN: (纸本)9798350386226

In the field of graph representation learning for molecular property prediction, self-supervised learning is highly appreciated for its ability to mine potential connections in unlabeled data. However, existing approaches such as neighborhood prediction or contrastive learning often lack in fusing structural and semantic features of molecular graphs. To address this issue, we propose a Graph Matching Based Graph Self-Supervised Learning (GMSSL) framework, which aims to comprehensively learn the structure and semantics of molecular graphs. Specifically, utilizing intra-graph and inter-graph convolution and graph matching methods, GMSSL is able to encode complex information for efficient training without supervision. After pre-training and fine-tuning, GMSSL demonstrates powerful molecular property prediction on multiple datasets. © 2024 IEEE.

关键词： Contrastive Learning

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