检索结果-内蒙古大学图书馆

SSRN 2022年

作者： Gong, Zhengze Pan, Xipeng Han, Chu Qiu, Bingjiang Zhao, Bingchao Liu, Yu Chen, Xinyi Liu, Wenbin Chen, Zhihua Lu, Cheng Liu, Zaiyi Fang, Gang Institute of Computing Science and Technology Guangzhou University Guangzhou510006 China Department of Radiology Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China Guangdong Cardiovascular Institute Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China

Background and Objective: The segmentation of oropharyngeal epithelium tissue within Haematoxylin&Eosin(HE)-stained pathological images is of great clinical significance for the pathological analysis and diagnosis. However, due to the imperfection of staining, staining fainting, or variations between staining methods in different labs, the contrast between epithelium and stroma may not be obvious in the HE-stained *** and methods: To address these challenges, we propose a two-step method, including a stain-style transfer step and a threshold segmentation step. In the first step, we propose a stain-style transfer network named CS-Net, together with our proposed attention module named CS-Gate, to convert the HE-stained image into an Immunohistochemistry (IHC)-stained image, aiming to enhance the appearance of epithelial tissue. In the second step, we perform threshold segmentation on the synthetic IHC-stained image to finally obtain a binary mask of oropharyngeal epithelium ***: We conduct experiments on Oropharyngeal Cancer Tissue-Microarray image dataset, and the results show that CS-Net can generate a more precise binary mask than UNet-based segmentation networks(achieving an accuracy of 92.48%), while restoring colors and details better than general GAN-based networks (about 10% higher in Structural Similarity). Then we use Breast Cancer Cases (BCSS) public dataset as external validation, and find out that CS-Net has a stronger generalization capability. The code of CS-Net is available at https://***/frankie98kung/CS-Net. © 2022, The Authors. All rights reserved.

关键词： Tissue

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On the Detection of Shilling Attacks in Federated Collaborative Filtering

On the Detection of Shilling Attacks in Federated Collaborat...

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Reliable Distributed Systems

作者： Yangfan Jiang Yipeng Zhou Di Wu Chao Li Yan Wang School of Data and Computer Science Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing (Sun Yat-sen University) Ministry of Education China Macquarie University Sydney NSW Australia Tencent Inc Shenzhen China

ISBN: (数字)9781728176260

ISBN: (纸本)9781728176277

Federated collaborative filtering (Fed-CF) is a variant of federated learning (FL) models, which can protect user privacy in recommender systems. In Fed-CF, the recommendation model is collectively trained across multiple decentralized clients by exchanging gradients only. However, the decentralized nature of Fed-CF makes it vulnerable to shilling attacks, which can be realized by inserting fake ratings of target items to distort recommendation results. Unfortunately, previous detection algorithms cannot work well in the FL framework, as all original data samples are not disclosed at all. In this paper, we are the first to systematically study the problem of shilling attacks in the context of federated learning, and propose an effective detection method called Federated Shilling Attack Detector (FSAD) to detect shilling attackers in Fed-CF. We first show the feasibility of shilling attacks in Fed-CF. Next, we dedicatedly design four novel features based on exchanged gradients among clients. By incorporating these gradient-based features, we train a semi-supervised Bayes classifier to identify shilling attackers effectively. Finally, we conduct extensive experiments based on real-world datasets to evaluate the performance of our proposed FSAD method. The experimental results show that FSAD can detect shilling attackers in Fed-CF with high accuracy, with the F 1 value as high as 0.90 on the Netflix dataset, which approaches the performance of the optimal detector that utilizes complete private user information for detection.

关键词： Privacy Collaborative filtering Detectors Collaborative work Information filters Reliability Recommender systems

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Observer-Based Robust Containment Control of Multi-agent Systems With Input Saturation

Observer-Based Robust Containment Control of Multi-agent Sys...

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Chinese Control Conference (CCC)

作者： Juan Qian Xiaoling Wang Guo-Ping Jiang Housheng Su College of Automation and College of Artificial Intelligence Nanjing University of Posts and Telecommunications and Jiangsu Engineering Lab for IOT Intelligent Robots(IOTRobot) Nanjing PR China School of Artificial Intelligence and Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry ofChina Huazhong University of Science and Technology Wuhan PR China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

In this paper, the robust containment control problem of the leader-following multi-agent systems with input saturation and input additive disturbance is addressed, where the followers can be informed by multiple leaders. With the help of the lowand-high gain feedback technique and the high-gain observer approach, a distributed control algorithm for each agent is firstly designed by using the observed output information, then sufficient conditions are provided to guarantee the semi-global robust containment of the system. Finally, some numerical simulations are given to verify the correctness of the theoretical results.

关键词： Multi-agent systems Robustness Observers Electronic mail State feedback Additives

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Relationship between pulmonary nodule malignancy and surrounding pleurae, airways and vessels: a quantitative study using the public LIDC-IDRI dataset

arXiv

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arXiv 2021年

作者： Qin, Yulei Gu, Yun Zhang, Hanxiao Yang, Jie Wang, Lihui Wang, Zhexin Yao, Feng Zhu, Yue-Min Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China CREATIS INSA Lyon CNRS UMR 5220 INSERM U1206 Université de Lyon Villeurbanne69621 France Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province School of Computer Science and Technology Guizhou University Guiyang550025 China Department of Thoracic Surgery Shanghai Chest Hospital Shanghai Jiao Tong University Shanghai200025 China

Objectives: To investigate whether the pleurae, airways and vessels surrounding a nodule on non-contrast computed tomography (CT) can discriminate benign and malignant pulmonary nodules. Materials and Methods: The LIDC-IDRI dataset, one of the largest publicly available CT database, was exploited for study. A total of 1556 nodules from 694 patients were involved in statistical analysis, where nodules with average scorings 3 were respectively denoted as benign and malignant. Besides, 339 nodules from 113 patients with diagnosis ground-truth were independently evaluated. Computer algorithms were developed to segment pulmonary structures and quantify the distances to pleural surface, airways and vessels, as well as the counting number and normalized volume of airways and vessels near a nodule. Odds ratio (OR) and Chi-square (χ2) testing were performed to assess the correlation between features of surrounding structures and nodule malignancy. A non-parametric receiver operating characteristic (ROC) analysis was conducted in logistic regression to evaluate discrimination ability of each structure. Results: For the benign and malignant groups, the average distances from nodules to pleural surface, airways and vessels are respectively (6.56, 5.19), (37.08, 26.43) and (1.42, 1.07) mm. The correlation between nodules and the counting number of airways and vessels that contact or project towards nodules are respectively (OR=22.96, χ2=105.04) and (OR=7.06, χ2=290.11). The correlation between nodules and the volume of airways and vessels are (OR=9.19, χ2=159.02) and (OR=2.29, χ2=55.89). The areas-under-curves (AUCs) for pleurae, airways and vessels are respectively 0.5202, 0.6943 and 0.6529. Conclusion: Our results show that malignant nodules are often surrounded by more pulmonary structures compared with benign ones, suggesting that features of these structures could be viewed as lung cancer biomarkers. © 2021, CC BY.

关键词： Computerized tomography

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An Interpretable Machine Learning Model Assists In Predicting Induction Chemotherapy Response and Survival for Locoregionally Advanced Nasopharyngeal Carcinoma Using MRI: A Multi-Center Study

SSRN

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SSRN 2024年

作者： Liao, Hai Chen, Xiao-Bo Zhao, Yang Pei, Wei Xia, Huang Wei, Wei Lai, Peng-Hao Jin, Wei-Feng Bao, Hua-Yan Liang, Xue-Li Xiao, Lei Chen, Zhen-Yu Lu, Shao-Lu Su, Dan-Ke Lu, Bing-Feng Pan, Ling-Hui Guangxi Nanning China Southern Medical University Guangdong Guangzhou China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Guangzhou China Department of Radiology Wuzhou Red Cross Hospital Guangxi Wuzhou China The First School of Clinical Medicine Zhejiang Chinese Medical University Hangzhou China College of Pharmaceutical Science Zhejiang Chinese Medical University Zhejiang Hangzhou China Department of Radiology The Second Affiliated Hospital of Guangxi Medical University Guangxi Nanning China Department of Anesthesiology Guangxi Medical University Cancer Hospital Guangxi Engineering Research Center for Tissue & Organ Injury and Repair Medicine Guangxi Health Commission Key Laboratory of Basic Science and Prevention of Perioperative Organ Disfunction Guangxi Clinical Research Center for Anesthesiology Guangxi Nanning China Department of Anesthesiology Guangxi Medical University Cancer Hospital Guangxi Engineering Research Center for Tissue & Organ Injury and Repair Medicine Guangxi Health Commission Key Laboratory of Basic Science and Prevention of Perioperative Organ Disfunction Guangxi Clinical Research Center for Anesthesiology No. 651 HeDi Road Guangxi Nanning530021 China Department of Radiology The Second Affiliated Hospital of Guangxi Medical University No.166 DaXuedong Road Guangxi Nanning530007 China Department of Radiology Guangxi Medical University Cancer Hospital No. 651 HeDi Road Guangxi Nanning530021 China Department of Radiology Wuzhou Red Cross Hospital No.3-1 XinXing Road Guangxi Wuzhou543002 China

Background: Variations of induction chemotherapy (ICT) response and survival are frequently observed in locoregionally advanced nasopharyngeal carcinoma (LANPC) patients. It remains uncertain which patients can benefit more from ICT and have a better outcome. We aimed to develop and validate an interpretable and generalized machine learning (ML) model using MRI for the individualized prediction of ICT response and survival in LANPC via a multi-center setting, and to further verify if the model could improve the predictive performance of doctors in clinical ***: A total of 1368 patients who underwent 1.5 or 3.0T MRI examination before ICT were enrolled from three hospitals between July 2015 and April 2020. An optimal ML model among logistic regression, support vector machine, random forest, artificial neural network, and genetic algorithm-enhanced artificial neural network (GNN) was developed in the training cohort (3.0 T, n=735), and the generalization performance of the model was assessed in the internal (3.0 T, n=243), external (3.0 T, n=99), and cross-field strength (1.5T, n=291) validation cohorts. Shapley Additive exPlanations (SHAP) and the image visualization technique (IVT) were used to enhance the interpretability of the model. An artificial intelligence (AI)-assisted experiment was designed to evaluate the performance of junior and senior doctors in predicting ICT response with and without the aid of the ***: Among the five ML models, the GNN model, based on 15 radiomics features, achieved optimal generalization performance in predicting ICT response, with areas under the curve (AUCs) ranging from 0.808 to 0.864 across the four cohorts. Survival analysis demonstrated that low-risk patients defined by the integration of the GNN-radiomics signature and clinical factors had better progression-free survival than high-risk patients in all four cohorts (hazard ratio ranging from 3.231 to 12.787, all log-rank P Interpretation: An interpret

关键词： Magnetic resonance imaging

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Channel and Trials Selection for Reducing Covariate Shift in EEG-based Brain-Computer Interfaces

Channel and Trials Selection for Reducing Covariate Shift in...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： He He Dongrui Wu Key Laboratory for Image Processing and Intelligent Control Ministry of Education and the School of Artificial Intelligence and Automation Huazhong University of Science and Technology

ISBN: (纸本)9781728145709

This paper aims at reducing the calibration effort of EEG-based brain-computer interfaces (BCIs). More specifically, in the context of cross-subject classification, we correct covariate shift of EEG data from different subjects, so that a classifier trained on auxiliary subjects can also be applied to a new subject, without any labeled trials from the new subject. Methods: We propose two approaches to enhance the performance of a state-of-the-art Riemannian space transfer learning (TL) algorithm: 1) trials selection, which resamples trials from the auxiliary subjects so that they become more consistent with those of the new subject;and, 2) channel selection, which reduces the number of channels and hence makes the Riemannian space computations more accurate and efficient. Results: We tested the proposed approaches on two motor imagery datasets. The results verified that they can enhance the performance of the state-of-the-art TL algorithm. Conclusion and significance: Our proposed approaches make the state-of-the-art TL algorithm more effective and efficient.

关键词： Brain-Computer Interfaces experimentation Trials Computer interfaces Thallium Shift Classifiers biological control 2-channel

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Depth Guided Cross-modal Residual Adaptive Network for RGB-D Salient Object Detection

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Journal of Physics: Conference Series 2021年第1期1873卷

作者： Zhengyun Zhao Qingpeng Yang Shangqin Yang Jun Wang School of Artificial Intelligence Henan University Kaifeng Henan 475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng Henan 475004 China

Depth modal features can provide complementary information for salient object detection (SOD). Most of the existing RGB-D SOD methods focus on fully combining RGB and Depth modal features without distinguishing them. In this paper, we propose a new depth guided cross-modal residual adaptive network for RGB-D SOD. We use two independent resnet-50 to extract the features of the two modes respectively. Then the cross-modal channel-wise refinement module is designed to obtain complementary modal information. We design a crossmodal guided module to make complementary modal information guide RGB image feature extraction. Finally, the residual adaptive selection module is used to enhance the spatial mutual concerns between the two modal features to achieve multimodal information fusion. Experimental results show that our method can achieve a more reasonable fusion state of RGB and Depth, and verify the effectiveness of our final saliency model.

关键词：

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BoostTree and BoostForest for Ensemble Learning

arXiv

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arXiv 2020年

作者： Zhao, Changming Wu, Dongrui Huang, Jian Yuan, Ye Zhang, Hai-Tao Peng, Ruimin Shi, Zhenhua Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China The Autonomous Intelligence Unmanned Systems Engineering Research Center of Ministry of Education of China The State Key Lab of Digital Manufacturing Equipment and Technology Wuhan China

Bootstrap aggregating (Bagging) and boosting are two popular ensemble learning approaches, which combine multiple base learners to generate a composite model for more accurate and more reliable performance. They have been widely used in biology, engineering, healthcare, etc. This paper proposes BoostForest, which is an ensemble learning approach using BoostTree as base learners and can be used for both classification and regression. BoostTree constructs a tree model by gradient boosting. It increases the randomness (diversity) by drawing the cut-points randomly at node splitting. BoostForest further increases the randomness by bootstrapping the training data in constructing different BoostTrees. BoostForest generally outperformed four classical ensemble learning approaches (Random Forest, Extra-Trees, XGBoost and LightGBM) on 35 classification and regression datasets. Remarkably, BoostForest tunes its parameters by simply sampling them randomly from a parameter pool, which can be easily specified, and its ensemble learning framework can also be used to combine many other base learners. Copyright © 2020, The Authors. All rights reserved.

关键词： Classification (of information)

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Deep reinforcement learning with a stage incentive mechanism of dense reward for robotic trajectory planning

arXiv

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arXiv 2020年

作者： Peng, Gang Yang, Jin Khyam, Mohammad Omar Key Laboratory of Image Processing and Intelligent Control Ministry of EducationB School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Automation South East University Nanjing China School of Engineering and Technology Central Queensland University Melbourne Australia

To improve the efficiency of deep reinforcement learning (DRL)-based methods for robot manipulator trajectory planning in random working environments, we present three dense reward functions. These rewards differ from the traditional sparse reward. First, a posture reward function is proposed to speed up the learning process with a more reasonable trajectory by modeling the distance and direction constraints, which can reduce the blindness of exploration. Second, a stride reward function is proposed to improve the stability of the learning process by modeling the distance and movement distance of joint constraints. Finally, in order to further improve learning efficiency, we are inspired by the cognitive process of human behavior and propose a stage incentive mechanism, including a hard stage incentive reward function and a soft stage incentive reward function. Extensive experiments show that the soft stage incentive reward function is able to improve the convergence rate by up to 46.9% with the state-of-the-art DRL methods. The percentage increase in the convergence mean reward was 4.4-15.5% and the percentage decreases with respect to standard deviation were 21.9-63.2%. In the evaluation experiments, the success rate of trajectory planning for a robot manipulator reached 99.6%. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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ÆMMamba: An Efficient Medical Segmentation Model With Edge Enhancement

引用

IEEE journal of biomedical and health informatics 2025年 PP卷 PP页

作者： Xingbo Dong Bowen Zhou Chen Yin Iman Yi Liao Zhe Jin Zhaozhao Xu Bin Pu Anhui University Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging Hefei 230093 China Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen China Nanchang Hangkong University Jiangxi Provincial Key Laboratory of Image Processing and Pattern Recognition Nanchang 330063 China University of Nottingham Malaysia Campus School of Computer Science Semenyih 43500 Malaysia Henan Polytechnic University School of Computer Science and Technology Jiaozuo China Hunan University College of Computer Science and Electronic Engineering Changsha China

Medical image segmentation is critical for disease diagnosis, treatment planning, and prognosis assessment, yet the complexity and diversity of medical images pose significant challenges to accurate segmentation. While Convolutional Neural Networks capture local features and Vision Transformers excel in the global context, both struggle with efficient long-range dependency modeling. Inspired by Mamba's State Space Modeling efficiency, we propose ÆMMamba, a novel multi-scale feature extraction framework built on the Mamba backbone network. AÆMMamba integrates several innovative modules: the Efficient Fusion Bridge (EFB) module, which employs a bidirectional state-space model and attention mechanisms to fuse multi-scale features; the Edge-Aware Module (EAM), which enhances low-level edge representation using Sobel-based edge extraction; and the Boundary Sensitive Decoder (BSD), which leverages inverse attention and residual convolutional layers to handle cross-level complex boundaries. ÆMMamba achieves state-of-the-art performance across 8 medical segmentation datasets. On polyp segmentation datasets (Kvasir, ClinicDB, ColonDB, EndoScene, ETIS), it records the highest mDice and mIoU scores, outperforming methods like MADGNet and Swin-UMamba, with a standout mDice of 72.22 on ETIS, the most challenging dataset in this domain. For lung and breast segmentation, ÆMMamba surpasses competitors such as H2Former and SwinUnet, achieving Dice scores of 84.24 on BUSI and 79.83 on COVID-19 Lung. And on the LGG brain MRI dataset, ÆMMamba attains an mDice of 87.25 and an mIoU of 79.31, outperforming all compared methods. The source code will be released at https://***/xingbod/eMMamba.

关键词： boundary-sensitive edge-aware feature fusion Medical image segmentation state-space model

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