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Deep Learning Ultrasound Computed Tomography Under Sparse Sampling

IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL 2023年第9期70卷 1084-1100页

作者： Long, Xiaoyun Chen, Junying Liu, Weiyong Tian, Chao Univ Sci & Technol China Sch Engn Sci Hefei 230026 Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei 230088 Anhui Peoples R China South China Univ Technol Sch Software Engn Guangzhou 510006 Peoples R China SCUT Minist Educ Key Lab Big Data & Intelligent Robot Guangzhou 510006 Peoples R China Univ Sci & Technol China USTC Affiliated Hosp 1 Dept UltrasoundDiv Life Sci & Med Hefei 230001 Peoples R China

Ultrasound computed tomography (USCT) is a fast-emerging imaging modality that is expected to help detect and characterize breast tumors by quantifying the distribution of the speed of sound (SOS) and acoustic attenuation in breast tissue. High-quality quantitative SOS reconstruction in USCT requires a large number of transducers, which incurs high system costs and slow computation. In contrast, sparsely distributed arrays are low-cost and fast but significantly degrade image quality. Thus, we propose a framework to achieve high-quality SOS reconstruction under sparse sampling based on a convolutional neural network (SRSS-Net) with faster computation. We first apply the bent-ray algorithm to sparsely sampled data and then apply the SRSS-Net to efficiently improve the image quality. Experimental results on synthetic and real datasets demonstrate that the proposed SRSS-Net provides reconstructions that are superior to those of state-of-the-art methods in terms of artifact suppression, structural preservation, quantitative restoration, and computational speed. As demonstrated in our experiments, the fine-tuning training strategy is suggested when applying SRSS-Net to real-world circumstances. The imaging and computational performance of SRSS-Net on the inhomogeneous breast phantom further demonstrates that SRSS-Net has great potential in real-time breast cancer detection.

关键词： Deep learning image reconstruction sparse view speed of sound (SOS) ultrasound computed tomography (USCT)

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LSU-NET: Lightweight Automatic Organs Segmentation Network for Medical Images

LSU-NET: Lightweight Automatic Organs Segmentation Network f...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yujie Ding Shenghua Teng Zuoyong Li Xiao Chen College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Minjiang University Fuzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

UNet and its variants have widespread applications in medical image segmentation. However, the substantial number of parameters and computational complexity of these models make them less suitable for use in clinical settings with limited computational resources. To address this limitation, we propose a novel Lightweight Shift U-Net (LSU-Net). We integrate the Light Conv Block and the Tokenized Shift Block in a lightweight manner, combining them with a dynamic weight multi-loss design for efficient dynamic weight allocation. The Light Conv Block effectively captures features with a low parameter count by combining standard convolutions with depthwise separable convolutions. The Tokenized Shift Block optimizes feature representation by shifting and capturing deep features through a combination of the Spatial Shift Block and depthwise separable convolutions. Dynamic adjustment of the loss weights at each layer approaches the optimal solution and enhances training stability. We validated LSU-Net on the UWMGI and MSD Colon datasets, and experimental results demonstrate that LSU-Net outperforms most state-of-the-art segmentation architectures.

关键词： Training Image segmentation Convolution Dynamic scheduling Stability analysis Resource management Speech processing Standards Monitoring Biomedical imaging

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Explainable Application Intent for Zero-Touch Networking: An Incorporation of Hypergraph and Transformer

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IEEE Transactions on Communications 2025年

作者： Wu, Bing Zou, Sai Liwang, Minghui Ni, Wei Wang, Xianbin Guizhou University College of Big Data and Information Engineering China National Key Laboratory of Autonomous Intelligent Unmanned Systems Department of Control Science and Engineering China Tongji University Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai China Data61 CSIRO Australia Department of Electrical and Computer Engineering Western University Canada

The autonomous interpretation of application intent (APPI) represents the primary step towards achieving closed-loop autonomy in zero-touch networking (ZTN) and also a prerequisite for intent-based networking (IBN). However, understanding APPIs and invoking the corresponding network resources require network professionals with extensive technical expertise to customize network service requests (NSRs), which presents significant challenges for the large-scale deployment of ZTN. This paper investigates an interesting problem of autonomous interpretation of APPIs for ZTN, where a novel mechanism integrating hypergraph and transformer with completeness assurance (HyperTrans-CA) is proposed. In particular, we first involve the Bayesian theory to model APPIs interpretability as maximizing the correct transition probability, where hypergraph is used to describe the complex relationship between application characteristics (e.g., scenario function, and performance) and NSRs, including network devices, virtual network functions (VNFs), and resources. Then, the hypergraph is integrated into the encoder, decoder, and attention mechanisms of Transformer, and a completeness assurance mechanism is designed to improve the prediction accuracy. The convergence of HyperTrans-CA and the corresponding convergence speed of the hypergraph-boosted Transformer in the graph search process are also analyzed. Comprehensive simulations and empirical measurements regarding industrial internet demonstrate that HyperTrans-CA can effectively explain/understand APPIs. Compared to the state-of-the-art Transformer and ChatGPT3.5 models, HyperTrans-CA improves the prediction accuracy of APPIs mapped to VNFs by 23% and 46%, respectively, while raising the prediction accuracy of VNF locations by 8.6 and 17.3 times. © 2025 IEEE.

关键词： Bayesian networks

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SS4Rec: Continuous-Time Sequential Recommendation with State Space Models

arXiv

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arXiv 2025年

作者： Xiao, Wei Wang, Huiying Zhou, Qifeng Department of Automation Xiamen University Xiamen361005 China School of Engineering Westlake University Hangzhou China Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision-making Xiamen University Xiamen China

Sequential recommendation is a key area in the field of recommender systems aiming to model user interest based on historical interaction sequences with irregular intervals. While previous recurrent neural network-based and attention-based approaches have achieved significant results, they have limitations in capturing system continuity due to the discrete characteristics. In the context of continuous-time modeling, state space model (SSM) offers a potential solution, as it can effectively capture the dynamic evolution of user interest over time. However, existing SSM-based approaches ignore the impact of irregular time intervals within historical user interactions, making it difficult to model complexed user-item transitions in sequences. To address this issue, we propose a hybrid SSM-based model called SS4Rec for continuous-time sequential recommendation. SS4Rec integrates a time-aware SSM to handle irregular time intervals and a relation-aware SSM to model contextual dependencies, enabling it to infer user interest from both temporal and sequential perspectives. In the training process, the time-aware SSM and the relation-aware SSM are discretized by variable stepsizes according to user interaction time intervals and input data, respectively. This helps capture the continuous dependency from irregular time intervals and provides time-specific personalized recommendations. Experimental studies on five benchmark datasets demonstrate the superiority and effectiveness of SS4Rec. Copyright © 2025, The Authors. All rights reserved.

关键词： Continuous time systems

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TrustUQA: A Trustful Framework for Unified Structured data Question Answering 39

TrustUQA: A Trustful Framework for Unified Structured Data Q...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Wen Jin, Long Zhu, Yushan Chen, Jiaoyan Huang, Zhiwei Wang, Junjie Hua, Yin Liang, Lei Chen, Huajun Zhejiang University China University of Manchester United Kingdom Ant Group China ZJU-Ant Group Joint Lab of Knowledge Graph China Zhejiang Key Laboratory of Big Data Intelligent Computing China

ISBN: (纸本)157735897X

Natural language question answering (QA) over structured data sources such as tables and knowledge graphs have been widely investigated, especially with Large Language Models (LLMs) in recent years. The main solutions include question to formal query parsing and retrieval-based answer generation. However, current methods of the former often suffer from weak generalization, failing to dealing with multi-types of sources, while the later is limited in trustfulness. In this paper, we propose TrustUQA, a trustful QA framework that can simultaneously support multiple types of structured data in a unified way. To this end, it adopts an LLM-friendly and unified knowledge representation method called Condition Graph (CG), and uses an LLM and demonstration-based two-level method for CG querying. For enhancement, it is also equipped with dynamic demonstration retrieval. We have evaluated TrustUQA with 5 benchmarks covering 3 types of structured data. It outperforms 2 existing unified structured data QA methods. In comparison with the baselines that are specific to one data type, it achieves state-of-the-art on 2 of the datasets. Further more, we have demonstrated the potential of our method for more general QA tasks, QA over mixed structured data and QA across structured data. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Structured Query Language

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Towards Accurate and Compact Architectures via Neural Architecture Transformer

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022年第10期44卷 6501-6516页

作者： Guo, Yong Zheng, Yin Tan, Mingkui Chen, Qi Li, Zhipeng Chen, Jian Zhao, Peilin Huang, Junzhou South China Univ Technol Sch Software Engn Guangzhou 510006 Peoples R China Peng Cheng Lab Shenzhen 518066 Peoples R China Tencent Weixin Grp Shenzhen 518054 Peoples R China South China Univ Technol Key Lab Big Data & Intelligent Robot Minist Educ Guangzhou 510006 Peoples R China Tencent Tencent AI Lab Shenzhen 518054 Peoples R China

Designing effective architectures is one of the key factors behind the success of deep neural networks. Existing deep architectures are either manually designed or automatically searched by some Neural Architecture Search (NAS) methods. However, even a well-designed/searched architecture may still contain many nonsignificant or redundant modules/operations (e.g., some intermediate convolution or pooling layers). Such redundancy may not only incur substantial memory consumption and computational cost but also deteriorate the performance. Thus, it is necessary to optimize the operations inside an architecture to improve the performance without introducing extra computational cost. To this end, we have proposed a Neural Architecture Transformer (NAT) method which casts the optimization problem into a Markov Decision Process (MDP) and seeks to replace the redundant operations with more efficient operations, such as skip or null connection. Note that NAT only considers a small number of possible replacements/transitions and thus comes with a limited search space. As a result, such a small search space may hamper the performance of architecture optimization. To address this issue, we propose a Neural Architecture Transformer++ (NAT++) method which further enlarges the set of candidate transitions to improve the performance of architecture optimization. Specifically, we present a two-level transition rule to obtain valid transitions, i.e., allowing operations to have more efficient types (e.g., convolution -> separable convolution) or smaller kernel sizes (e.g., 5x5 -> 3x3). Note that different operations may have different valid transitions. We further propose a Binary-Masked Softmax (BMSoftmax) layer to omit the possible invalid transitions. Last, based on the MDP formulation, we apply policy gradient to learn an optimal policy, which will be used to infer the optimized architectures. Extensive experiments show that the transformed architectures significantly outperform

关键词： Architecture optimization neural architecture search compact architecture design operation transition

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Differences in pseudogene evolution contributed to the contrasting flavors of turnip and Chiifu, two Brassica rapa subspecies

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Plant Communications 2023年第1期4卷 120-135页

作者： Xin Yin Danni Yang Youjie Zhao Xingyu Yang Zhili Zhou Xudong Sun Xiangxiang Kong Xiong Li Guangyan Wang Yuanwen Duan Yunqiang Yang Yongping Yang Plant Germplasm and Genomics Center Kunming Institute of BotanyChinese Academy of SciencesKunming 650201China Key Laboratory for Plant Diversity and Biogeography of East Asia Kunming Institute of BotanyChinese Academy of SciencesKunming 650204China Institute of Tibetan Plateau Research at Kunming Kunming Institute of BotanyChinese Academy of SciencesKunming 650201China College of Big Data and Intelligent Engineering Southwest Forestry UniversityKunmingYunnanChina University of Chinese Academy of Sciences Beijing 100049China

Pseudogenes are important resources for investigation of genome evolution and genomic diversity because they are nonfunctional but have regulatory effects that influence plant adaptation and ***,few systematic comparative analyses of pseudogenes in closely related species have been ***,we present a turnip(Brassica rapa ***)genome sequence and characterize pseudogenes among diploid Brassica species/*** results revealed that the number of pseudogenes was greatest in Brassica oleracea(CC genome),followed by ***(AA genome)and then Brassica nigra(BB genome),implying that pseudogene differences emerged after species *** Brassica AA genomes,pseudogenes were distributed asymmetrically on chromosomes because of numerous chromosomal insertions/rearrangements,which contributed to the diversity among *** differences among subspecies were reflected in the flavor-related glucosinolate(GSL)***,turnip had the highest content of pungent substances,probably because of expansion of the methylthioalkylmalate synthase-encoding gene family in turnips;these genes were converted into pseudogenes in *** ***(Chiifu).RNA interference-based silencing of the gene encoding 2-oxoglutarate-dependent dioxygenase 2,which is also associated with flavor and anticancer substances in the GSL pathway,resulted in increased abundance of anticancer compounds and decreased pungency of turnip and *** findings revealed that pseudogene differences between turnip and Chiifu influenced the evolution of flavor-associated GSL metabolism-related genes,ultimately resulting in the different flavors of turnip and Chiifu.

关键词： turnip genome comparative genomics pseudogene evolution GSL biosynthesis flavor

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Domain compensatory adversarial networks for partial domain adaptation

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第7期80卷 11255-11272页

作者： Huang, Junchu Zhang, Pengyu Zhou, Zhiheng Fan, Kefeng South China Univ Technol Sch Elect & Informat Engn Guangzhou Peoples R China South China Univ Technol Key Lab Big Data & Intelligent Robot Minist Educ Guangzhou Peoples R China China Elect Standardizat Inst Beijing 100007 Peoples R China

Recently, domain adaptation has stimulated interest in the community of machine learning since it can improve the performance of the model in a new domain (target domain) by borrowing knowledge from a labeled source domain. At the same time, the presence of large-scale labeled datasets also raised significant attention in this scenario: the class labels in the new domain are only a subset of those in the source domain. We propose an adversarial-net-based method in this paper, called domain compensatory adversarial network (DCAN). The critical difficulty of this problem is to reduce the negative impact of source instances with weak discriminability and filter out outlier source classes by exploiting the prior probability of classes. DCAN can identify source instances with weak discriminability and reverse its domain label to compensate for the target label space, which alleviates the negative effect of mismatching label space. Besides, DCAN reweights outlier source classes with the class prior distributions of source data for both category classifier and domain classifier to promote positive transfer. Experiments have revealed the superiority of DCAN compared to the existing methods.

关键词： Domain adaptation Deep learning Image classification

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Federated Hybrid-Supervised Learning for Universal Medical Image Segmentation

Federated Hybrid-Supervised Learning for Universal Medical I...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shenhai Zheng Sian Wen Congyu Li Qing Chen Laquan Li College of Computer Science and Technology Chongqing University of Posts and Telecommunications China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education China School of Science Chongqing University of Posts and Telecommunications China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Federated Learning (FL) is an advanced technology that tackles the challenge of blocked data arising from privacy concerns, enabling the training of deep learning models without the need for data sharing. However, FL faces difficulties with heterogeneous data and limited annotations in medical image segmentation. Motivated by this discovery, this study proposes a novel hybrid-supervised federated learning method (FedSLAG) that explores various types of annotations in medical imaging. To focus more on weakly-supervised and unsupervised scenarios within hybrid-supervised learning, a federated Gaussian enhancement module was proposed for heterogeneous sparse annotations (points and scribbles). The feature extraction module combines the features of multiple weakly-supervised clients and establishes the correlation of similar pixels, thus making up for the deficiency of scarce annotations and the insufficient feature extraction capability of a single machine. Then, a two-stage broadcast mechanism based on supervision sparsity was proposed to alleviate optimization deviation in local models. Experiments on breast tumor and skin lesion segmentation tasks demonstrate significant efficiency gains as well as highly competitive segmentation accuracy in many hybrid-supervised situations. Our codes are available at: https://***/TrivenDev/FedSLAG.

关键词： Training Image segmentation Breast tumors Annotations Federated learning Feature extraction Skin Lesions Speech processing Biomedical imaging

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Improved Fall-down Detection Algorithm: FE-YOLO

Improved Fall-down Detection Algorithm: FE-YOLO

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Chinese Control and Decision Conference, CCDC

作者： Yuchen Zhang Hongyan Ma Zongyuan Xie School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China School of Electrical and Information Engineering Institute of Distributed Energy Storage Safety Big Data Beijing Key Laboratory of Intelligent of Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

To address the concerns regarding dataset completeness and detection speed for common fall injuries in the elderly, a comprehensive dataset has been established. Additionally, a lightweight fall detection model named FE-YOLO, which is based on YOLOv8, has been developed. This model replaces the network’s internal feature extraction module with a more efficient Fasternet Block module and introduces an enhanced detection head that utilizes data sharing and grouped convolution techniques. When compared to the YOLOv8n model, the FE-YOLO model demonstrates a reduction in parameters by 43.1%, a decrease in GFLOPs by 51.9%, an improvement in model inference speed by 9.70%, and an increase in FPS by 10.5%. Notably, these enhancements do not significantly impact the average accuracy of the model. The efficiency of the FE-YOLO model renders it suitable for deployment on devices with limited computing power.

关键词： YOLO Convolution Computational modeling Feature extraction Inference algorithms data models Fall detection

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