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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xingyun Li Lin Lu Xinyu Yi Hao Wang Yunshao Zheng Yanhong Yu Qingxiang Wang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Mental Health Center Jinan China Shandong University of Traditional Chinese Medicine Jinan China

With the rise in societal pressures, depression and anxiety have increasingly become prominent mental health conditions impacting people’s lives. To enhance the efficacy of automatic detection for these disorders, we have developed an experimental framework called the Voluntary Facial Expression Mimicry(VFEM). This framework led to the creation of the VFEM Dataset, which supports related research endeavors. Subsequently, we introduce the LI-FPN designed specifically for the automatic identification of depression and anxiety disorders. The LI-FPN comprises two core components: the Learning and Imitation Module(LIM) and the Spatio-temporal Feature Pyramid Network(STFPN). Within the LIM, we leverage sequence features to facilitate comprehensive feature extraction through learning and imitation steps. The STFPN is designed to focus on outliers in multi-scale features for further screening. Compared with traditional attention methods, LI-FPN is more suitable for processing sequence data features and small sample datasets. Upon training using the VFEM Dataset, the LI-FPN achieves impressive accuracies: 0.850 for depression detection, 0.835 for anxiety detection, and 0.786 for co-occurrence detection of depression and anxiety. Meanwhile, LI-FPN also achieves SOAT results on AVEC2014 dataset. The source code for LI-FPN is accessible at https://***/muzixingyun/LI-FPN

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An Encoder-Decoder Network with Residual Convolution for Leukocyte Image Segmentation 7

An Encoder-Decoder Network with Residual Convolution for Leu...

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7th International Conference on information Science and Control Engineering, ICISCE 2020

作者： Chen, Yuanyuan Teng, Shenghua Li, Zuoyong Zhang, Fuquan Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Fuzhou350121 China Shandong University of Science and Technology College of Electronics and Information Engineering Qingdao266590 China

ISBN: (纸本)9781728164069

The appearance analysis and counting of peripheral blood leukocytes can assist the diagnosis of blood diseases such as leukemia. Therefore, it is necessary to automatically extract leukocytes from blood smear images. In this paper, we propose an end-to-end peripheral blood leukocyte image segmentation method based on fully convolutional neural network, which can not only encode multi-scale contextual information, but also capture clear boundary information through decoding. Specifically, the proposed method first utilizes the context-aware feature encoder with residual convolutions to extract a set of multi-scale features. Then, a refinement module of parallel dilated convolutions with multiple dilation rates for expanding the receptive field of the feature maps is introduced to aggregate contextual information. Finally, clear prediction results are reconstructed by the context-aware feature decoder and skip connections. We demonstrate the effectiveness of the proposed method on two publicly available datasets. © 2020 IEEE.

关键词： Convolution

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Cross-receptive Focused Inference Network for Lightweight Image Super-Resolution

arXiv

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arXiv 2022年

作者： Li, Wenjie Li, Juncheng Gao, Guangwei Deng, Weihong Zhou, Jiantao Yang, Jian Qi, Guo-Jun The Intelligent Visual Information Perception Laboratory Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing210046 China The Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China The School of Communication and Information Engineering Shanghai University Shanghai200444 China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing210094 China The Pattern Recognition and Intelligent System Laboratory School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China The State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science Faculty of Science and Technology University of Macau 999078 China The School of Computer Science and Technology Nanjing University of Science and Technology Nanjing210094 China The Research Center for Industries of the Future The School of Engineering Westlake University Hangzhou310024 China OPPO Research SeattleWA98101 United States

Recently, Transformer-based methods have shown impressive performance in single image super-resolution (SISR) tasks due to the ability of global feature extraction. However, the capabilities of Transformers that need to incorporate contextual information to extract features dynamically are neglected. To address this issue, we propose a lightweight Cross-receptive Focused Inference Network (CFIN) that consists of a cascade of CT Blocks mixed with CNN and Transformer. Specifically, in the CT block, we first propose a CNN-based Cross-Scale information Aggregation Module (CIAM) to enable the model to better focus on potentially helpful information to improve the efficiency of the Transformer phase. Then, we design a novel Cross-receptive Field Guided Transformer (CFGT) to enable the selection of contextual information required for reconstruction by using a modulated convolutional kernel that understands the current semantic information and exploits the information interaction within different self-attention. Extensive experiments have shown that our proposed CFIN can effectively reconstruct images using contextual information, and it can strike a good balance between computational cost and model performance as an efficient model. Source codes will be available at https://***/IVIPLab/CFIN. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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Liver Tumor Segmentation of CT Image by Using Deep Fully Convolutional Network 1

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3rd International Conference on Machine Learning for Cyber Security, ML4CS 2020

作者： Jin, Lingmin Ma, Rui Zhao, Meng Teng, Shenghua Li, Zuoyong College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China

ISBN: (数字)9783030622237

ISBN: (纸本)9783030622220

Accurate segmentation of liver tumors is an important guarantee for the success of liver cancer surgery, where convolutional network has been a type of popular method. However, the performance of the traditional convolutional network is limited by the network depth. To improve the accuracy of liver tumor segmentation, we propose a cascaded deep fully convolutional network (DFCN) which uses ResNet as the basis network followed by side output layer in the upsampling stage to fuse multi-scale image features. For better localizing the liver tumors, the segmentation result is further refined by a fully connected conditional random field. Experimental results show that the proposed method achieves higher segmentation accuracy than several state-of-the-art methods. © 2020, Springer Nature Switzerland AG.

关键词： Convolution

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A QoE Evaluation and Adaptation Method for Multi-player Online Games 14th

A QoE Evaluation and Adaptation Method for Multi-player Onli...

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14th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2021

作者： Wang, Zaijian Guo, Wei Zhao, Le Yang, Weidong Mao, Shiwen Li, Zhipeng The School of Physics and Electronic Information Anhui Normal University Anhui Wuhu241002 China The Key Laboratory of Grain Information Processing and Control at Henan University of Technology Ministry of Education Henan Zhengzhou450001 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States Anhui Province Key Laboratory of Optoelectric Materials Science and Technology School of Physics and electronic information Anhui Normal University Wuhu241002 China

ISBN: (纸本)9783030898137

To evaluate the performance of multi-player online games (MPOGs) (e.g., the online Chinese poker game), this paper presents a novel Effective Quality of Experience (EQoE) concept that considers the interaction among multiple players. A novel QoE evaluation and adaptation method is proposed utilizing EQoE. Specifically, the proposed method assigns different weights to different players according to their roles in the multi-player online poker game. Some players with a lower weight are willing to reduce their own QoE to save network resources for players with a higher weight in order to keep the game going under poor network conditions. Our simulation study demonstrates the feasibility of the proposed QoE evaluation and adaptation method. © 2021, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Quality control

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Hand Hygiene Assessment via Joint Step Segmentation and key Action Scorer

arXiv

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arXiv 2022年

作者： Li, Chenglong Zhu, Qiwen Liu, Tubiao Tang, Jin Su, Yu Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Artificial Intelligence Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China Hefei Normal University Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230061 China

Hand hygiene is a standard six-step hand-washing action proposed by the World Health Organization (WHO). However, there is no good way to supervise medical staff to do hand hygiene, which brings the potential risk of disease spread. Existing action assessment works usually make an overall quality prediction on an entire video. However, the internal structures of hand hygiene action are important in hand hygiene assessment. Therefore, we propose a novel fine-grained learning framework to perform step segmentation and key action scorer in a joint manner for accurate hand hygiene assessment. Existing temporal segmentation methods usually employ multi-stage convolutional network to improve the segmentation robustness, but easily lead to over-segmentation due to the lack of the long-range dependence. To address this issue, we design a multi-stage convolution-transformer network for step segmentation. Based on the observation that each hand-washing step involves several key actions which determine the hand-washing quality, we design a set of key action scorers to evaluate the quality of key actions in each step. In addition, there lacks a unified dataset in hand hygiene assessment. Therefore, under the supervision of medical staff, we contribute a video dataset that contains 300 video sequences with fine-grained annotations. Extensive experiments on the dataset suggest that our method well assesses hand hygiene videos and achieves outstanding performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolution

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Advances in Embodied Navigation Using Large Language Models: A Survey

arXiv

引用

arXiv 2023年

作者： Lin, Jinzhou Gao, Han Feng, Xuxiang Xu, Rongtao Wang, Changwei Zhang, Man Guo, Li Xu, Shibiao School of Artificial Intelligence Beijing University of Posts and Telecommunications China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China Aerospace Information Research Institute Chinese Academy of Science China Qilu University of Technology Shandong Academy of Sciences China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science China

In recent years, the rapid advancement of Large Language Models (LLMs) such as the Generative Pre-trained Transformer (GPT) has attracted increasing attention due to their potential in a variety of practical applications. The application of LLMs with Embodied Intelligence has emerged as a significant area of focus. Among the myriad applications of LLMs, navigation tasks are particularly noteworthy because they demand a deep understanding of the environment and quick, accurate decision-making. LLMs can augment embodied intelligence systems with sophisticated environmental perception and decision-making support, leveraging their robust language and image-processing capabilities. This article offers an exhaustive summary of the symbiosis between LLMs and embodied intelligence with a focus on navigation. It reviews state-of-the-art models, research methodologies, and assesses the advantages and disadvantages of existing embodied navigation models and datasets. Finally, the article elucidates the role of LLMs in embodied intelligence, based on current research, and forecasts future directions in the field. A comprehensive list of studies in this survey is available at https://***/Rongtao-Xu/Awesome-LLM-EN. Copyright © 2023, The Authors. All rights reserved.

关键词： Navigation

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Real-Time Detection of Tea Chrysanthemums in Complex Outdoor Scenes

Real-Time Detection of Tea Chrysanthemums in Complex Outdoor...

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IEEE International Conference on Industrial technology (ICIT)

作者： Siyang Zang Lei Shu Ru Han Feiya Zhang Zhijun Zhang Der-Jiunn Deng College of Artificial Intelligence Nanjing Agricultural University Nanjing China Guangdong Provincial Key Laboratory for Green Agricultural Production and Intelligent Equipment School of Environmental Science and Engineering Guangdong University of Petrochemical Technology Maoming China School of Engineering University of Lincoln Lincoln U.K. Department of Computer Science and Information Engineering National Changhua University of Education Changhua Taiwan

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

Picking robots have emerged as a pivotal focus in smart agriculture, with precise target position detection being a key element for realizing robotic picking. However, detecting tea chrysanthemums presents various challenges, including their dynamic appearance, complex environmental conditions, susceptibility to occlusion and lighting variations, and the requirement for real-time embedded systems. When applied to complex outdoor scenes for Tea chrysanthemum targets, existing detection models suffer from drawbacks such as missed detections, low accuracy, and slow processing speeds. To address these challenges, we introduce YOLO-VTC (You Only Look Once-Varieties of Tea Chrysanthemum), a lightweight deep-learning model optimized for the Jetson Xavier NX platform. Detection experiments were conducted on image data capturing eight varieties of tea chrysanthemums in complex outdoor scenes collected in 2023. Results demonstrate that YOLO-VTC achieves an average accuracy of 86.60% and a peak accuracy of 97.10%, surpassing other lightweight networks with remarkable performance. Notably, the model effectively maintains a compact size of 6.58MB and has been successfully deployed on edge device platforms.

关键词： Adaptation models Technological innovation Accuracy Computational modeling Feature extraction Stability analysis Robustness Real-time systems Robots Load modeling

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Large-Scale Target Detection and Classification Based on Improved Candidate Regions 11th

Large-Scale Target Detection and Classification Based on Imp...

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11th International Conference on Image and Graphics, ICIG 2021

作者： Xi, Runping Han, Qianqian Jia, Gaoyun Kou, Xuefeng School of Computer Science and Techology Northwest Polytechnical University Xi’an710072 China National Engineering Laboratory of Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’an710129 China Shaanxi Provincial Key Laboratory of Speech and Image Information Processing Xi’an710129 China Shaanxi Baocheng Aviation instrument Co. Ltd. Baoji China

ISBN: (纸本)9783030873578

Target detection of aerial images has become a frontier subject of concern in the image processing field. Using existing method to detect and classify large-scale building objects in aerial images, the accuracy is still a little low. This is mainly because the current method does not make full use of the prior information of the target to be detected, so there are too much redundant information in the candidate box. In this paper, our own dataset were built and then utilize the Hough transform to filter out the images that may exist in the sequence image. For images with dense lines or circles, it is possible that there is an artificial building target which will be detected, otherwise it is excluded directly. Besides, this paper exploits significance analysis from the filtered image and then extract the area of interest where the potential target is located. The results of the above-mentioned processing lay a good foundation for the subsequent detection and classification which can help improve the accuracy. © 2021, Springer Nature Switzerland AG.

关键词： Antennas

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Cloud-magnetic resonance imaging system: In the era of 6G and artificial intelligence

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Magnetic Resonance Letters 2025年 57-68页

作者： Yirong Zhou Yanhuang Wu Yuhan Su Jing Li Jianyu Cai Yongfu You Jianjun Zhou Di Guo Xiaobo Qu Department of Electronic Science Intelligent Medical Imaging R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University Department of Electronic Science Key Laboratory of Digital Fujian on IoT Communication Xiamen University Shanghai Electric Group Co. Ltd China Telecom Group China Mobile Group Department of Radiology Zhongshan Hospital (Xiamen) Fudan University Xiamen Municipal Clinical Research Center for Medical Imaging Fujian Province Key Clinical Specialty Construction Project (Medical Imaging Department) Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial Intelligence School of Computer and Information Engineering Xiamen University of Technology

Magnetic resonance imaging(MRI) plays an important role in medical diagnosis,generating petabytes of image data annually in large *** voluminous data stream requires a significant amount of network bandwidth and extensive storage ***,local data processing demands substantial manpower and hardware *** isolation across different healthcare institutions hinders crossinstitutional collaboration in clinics and *** this work,we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud computing,6G bandwidth,edge computing,federated learning,and blockchain *** system is called Cloud-MRI,aiming at solving the problems of MRI data storage security,transmission speed,artificial intelligence(AI) algorithm maintenance,hardware upgrading,and collaborative *** workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw Data(ISMRMRD) ***,the data are uploaded to the cloud or edge nodes for fast image reconstruction,neural network training,and automatic ***,the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other *** Cloud-MRI system will save the raw imaging data,reduce the risk of data loss,facilitate inter-institutional medical collaboration,and finally improve diagnostic accuracy and work efficiency.

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