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arXiv 2025年

作者： Yang, Yiqiong Yuan, Yitian Ren, Baoxing Wu, Ye Feng, Yanqiu Zhang, Xinyuan School of Biomedical Engineering Southern Medical University Guangzhou510515 China Guangdong Provincial Key Laboratory of Medical Image Processing Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology Southern Medical University Guangzhou510515 China School of Computer Science and Technology Nanjing University of Science and Technology Nanjing China Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence & Key Laboratory of Mental Health The Ministry of Education Southern Medical University Guangzhou China Department of Radiology Shunde Hospital Southern Medical University The First People's Hospital of Shunde Foshan Foshan China

Diffusion MRI tractography technique enables non-invasive visualization of the white matter pathways in the brain. It plays a crucial role in neuroscience and clinical fields by facilitating the study of brain connectivity and neurological disorders. However, the accuracy of reconstructed tractograms has been a longstanding challenge. Recently, deep learning methods have been applied to improve tractograms for better white matter coverage, but often comes at the expense of generating excessive false-positive connections. This is largely due to their reliance on local information to predict long-range streamlines. To improve the accuracy of streamline propagation predictions, we introduce a novel deep learning framework that integrates image-domain spatial information and anatomical information along tracts, with the former extracted through convolutional layers and the later modeled via a Transformer-decoder. Additionally, we employ a weighted loss function to address fiber class imbalance encountered during training. We evaluate the proposed method on the simulated ISMRM 2015 Tractography Challenge dataset, achieving a valid streamline rate of 66.2%, white matter coverage of 63.8%, and successfully reconstructing 24 out of 25 bundles. Furthermore, on the multi-site Tractoinferno dataset, the proposed method demonstrates its ability to handle various diffusion MRI acquisition schemes, achieving a 5.7% increase in white matter coverage and a 4.1% decrease in overreach compared to RNN-based methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Image enhancement

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Exploration and Practice of Teaching Reform of computer Public Courses Under Big Data Environment

Exploration and Practice of Teaching Reform of Computer Publ...

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International Conference on Cyber Security Intelligence and Analytics, CSIA 2020

作者： Zhang, Hua Wang, Jiong Wu, Zhengyuan College of Computer and Control Engineering Minjiang University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China

ISBN: (纸本)9783030433055

computers are the necessary professional knowledge of modern talents, and universities offer computer public courses to meet the needs of social talents development. At present, there are many problems existing in the computer courses teaching in colleges and universities, such as loose content, lagging mode, slow feedback, etc., and the classroom teaching is not efficient. Based on the big data environment, applying information technology to the teaching of computer public courses can re-plan the course, improve the teaching mode, collect students’ teaching evaluation, and improve the teaching effect. The article will analyze the problems existing in the teaching process of computer public courses in China, and propose corresponding reform strategies, aiming at promoting the development of teaching in colleges and universities, improving the quality of talents and meeting the development needs of the times. © Springer Nature Switzerland AG 2020.

关键词： Big data

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LI-FPN: Depression and Anxiety Detection from Learning and Imitation

LI-FPN: Depression and Anxiety Detection from Learning and I...

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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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Peripheral Blood Leukocyte Image Segmentation Based on Deep Convolutional Neural Network 3rd

Peripheral Blood Leukocyte Image Segmentation Based on Deep ...

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

作者： Chen, Yuanyuan Teng, Shenghua Li, Zuoyong College of Electronics 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: (纸本)9783030624620

Counting and analysis of leukocyte on peripheral blood smears have great significance to assist doctors’ pathological diagnosis. Thus, accurate and automatic segmentation of leukocyte from blood smear images is a fundamental task. To achieve this task, we proposed an end-to-end peripheral blood leukocyte segmentation method based on deep convolutional neural networks, where the encoder-decoder network is its basic structure. Specifically, the proposed method first utilizes the feature encoder to extract multi-scale leukocyte features. Then, the context-aware feature decoder is introduced to reconstruct the segmentation mask of leukocyte. Finally, each layer generates a prediction map in the decoder module, and all these prediction maps are averaged to be the final prediction map. To demonstrate the effectiveness of the proposed method, we performed qualitative and quantitative comparisons with several existing methods on two publicly available datasets and a private dataset. Experimental results validate that the proposed method is superior for peripheral blood leukocyte image segmentation in terms of accuracy and robustness. © 2020, Springer Nature Switzerland AG.

关键词： Deep neural networks

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Cerebral Microbleeds Detection Based on 3D Convolutional Neural Network 1

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

作者： Zhao, Meng Jin, Chen Jin, Lingmin 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

Cerebral microbleeds (CMBs) are important imaging and diagnostic biomarkers for cerebrovascular diseases and cognitive dysfunctions. Reliable detection of the location and amount of CMBs in brain tissue is crucial for the diagnosis, prevention and treatment of related diseases, where traditional Convolutional Neural Network (CNN) has been applied but may fail to achieve high enough detection accuracy. To alleviate this issue, we utilize 3D Fully Convolutional Networks (FCN) and 3D AlexNet to establish a cascade coarse-to-fine detection manner. Specifically, CMBs candidates are first screened out using 3D FCN, followed by 3D AlexNet which extracts the spatial features of CMBs and distinguishes the false positive samples from candidate regions. Experimental results show that the proposed method can realize precise detection of CMBs in magnetic resonance images (MRI) by improving detection sensitivity and reducing false positive samples. © 2020, Springer Nature Switzerland AG.

关键词： Magnetic resonance

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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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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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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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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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Hand Hygiene Assessment via Joint Step Segmentation and key Action Scorer

arXiv

引用

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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