检索结果-内蒙古大学图书馆

IEEE International Conference on Bioinformatics and Biomedicine

作者： Wenjun Tan Jinzhu Yang Yao Liu Xiaoxia Yin Huangying Liu Yanchun Zhang Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Cyberspace Institute of Advanced Technology Guangzhou University Centre for Applied Informatics College of Engineering and Science Victoria University

ISBN: (纸本)9781728118680

Segmentation of lung parenchyma as a pretreatment step for the detection of some lung lesions requires high accuracy to provide more reliable information for lesion detection. In this paper, we proposed a segmentation method of lung parenchyma from chest CT images by Dual U-Net algorithm based on the characteristics of different lung region. Firstly, the original lung region is extracted from the full chest CT images of patient by an automatic threshold selection method. Then, the images of the different regions of lung is obtained by calculating the area. Finally, the Dual U-Net is trained according the different lung regions. The first U-Net algorithm operates these middle chest images and other images are operated by the second U-Net algorithm to reduce the interference of other images. The experimental results shown that the segmenting results of lung parenchyma by this method are more accurate than the results by single U-Net and U-Net++ algorithm.

关键词： Lung parenchyma segmentation U-Net chest CT images Lung Tissue Chest imaging chest ct images Lung Dual lung lesion

来源：评论

学校读者我要写书评

暂无评论

Nuclei Segmentation in Histopathology images Using Rotation Equivariant and Multi-level Feature Aggregation Neural Network

Nuclei Segmentation in Histopathology Images Using Rotation ...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yiqi Chen Xuanya Li Kai Hu Zhineng Chen Xieping Gao Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan China Baidu Inc Beijing China Chinese Academy of Sciences Institute of Automation Beijing China College of Medical Imaging and Inspection Xiangnan University Chenzhou China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

The histopathological analysis is the gold standard for assessing the presence and many complex diseases, like tumors. As one of the essential part of tumors, the shape, staining, and tissue distribution of the nuclei plays an important role in tumor diagnosis. However, due to nuclei congestion and possible occlusion, nuclei segmentation remains challenging. In this paper, we propose an automatic and effective nuclei segmentation method in histopathology images based on rotation equivariant and multi-level feature aggregation neural network (REMFANet). First, considering the inherent rotation equivariant of digital pathological images, we introduce group equivariant convolutions to improve the performance of the automatic segmentation of pathological images. Second, to eliminate the semantic gap between shallow features and deep features in encoder-decoder structural models, we propose a multi-level feature aggregation strategy based on U-Net 3+. Specifically, (1) we design a new decoder module to restore pixel-level predictions more accurately; (2) we propose an improved long-skip connection mode to provide richer semantic information in the decoder; (3) we also construct a semantic enhancement block to enhance the robustness of lowlevel semantic information. Finally, we evaluate our REMFA-Net on the MoNuSeg dataset and compare the results with seven state-of-the-art methods. Experimental results demonstrate the superiority of the proposed method over other models for the nuclei segmentation in histopathology images.

关键词： Semantics image segmentation Feature extraction Decoding Convolution Neural networks Training

来源：评论

学校读者我要写书评

暂无评论

Cancer nanomedicine meets immunotherapy: opportunities and challenges

引用

中国药理学报（英文版） 2020年第7期41卷 954-958页

作者： Qingxue Sun Xiangyang Bai Alexandros Marios Sofias Roy van der Meel Eduardo Ruiz-Hernandez Gert Storm Wim E.Hennink Bruno De Geest Fabian Kiessling Hai-jun Yu Twan Lammers Yang Shi Institute for Experimental Molecular Imaging Uniklinik RWTH Aachen and Helmholtz Institute for Biomedical Engineering Faculty of Medicine RWTH Aachen University AachenGermany Laboratory of Chemical Biology Department of Biomedical Engineering and Institute for Complex Molecular Systems Eindhoven University of Technology EindhovenThe Netherlands School of Pharmacy and Pharmaceutical Sciences Trinity College Dublin Dublin Ireland Department of Pharmaceutics Utrecht Institute for Pharmaceutical Sciences Utrecht University Utrecht The Netherlands Department of Targeted Therapeutics MIRA Institute for Biomedical Technology and Technical Medicine University of Twente Enschede The Netherlands Department of Pharmaceutics Ghent University Ghent Belgium Fraunhofer MEVlS Institute for Medical Image Computing AachenGermany State Key Laboratory of Drug Research & Center of Pharmaceutics Shanghai Institute of Materia Medica Chinese Academy of Sciences Shanghai 201203 China

Cancer nanomedicines have shown promise in combination immunotherapy,thus far mostly preclinically but also already in clinical *** nanomedicines with immunotherapy aims to reinforce the cancer-immunity cycle,via potentiating key steps in the immune reaction cascade,namely antigen release,antigen processing,antigen presentation,and immune cell-mediated *** nano-immunotherapy can be realized via three targeting strategies,i.e.,by targeting cancer cells,targeting the tumor immune microenvironment,and targeting the peripheral immune *** clinical potential of nano-immunotherapy has recently been demonstrated in a phase Ⅲ trial in which nano-albumin paclitaxel (Abraxane(R)) was combined with atezolizumab (Tecentriq(R)) for the treatment of patients suffering from advanced triple-negative breast *** the present paper,besides strategies and initial (pre)clinical success stories,we also discuss several key challenges in *** together,nanomedicines combined with immunotherapy are gaining significant attention,and it is anticipated that they will play an increasingly important role in clinical cancer therapy.

关键词： nanomedicine immunotherapy targeting combination therapy clinical translation

来源：评论

学校读者我要写书评

暂无评论

Learning data-adaptive non-parametric kernels

The Journal of Machine Learning Research

引用

The Journal of Machine Learning Research 2020年第1期21卷 8590-8628页

作者： Fanghui Liu Xiaolin Huang Chen Gong Jie Yang Li Li Department of Electrical Engineering ESAT-STADIUS KU Leuven Belgium Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China and Department of Computing Hong Kong Polytechnic University Hong Kong SAR China Department of Automation BNRist Tsinghua University China

In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an initial kernel matrix, a data-adaptive matrix with two constraints is imposed in an entry-wise scheme. Learning this data-adaptive matrix in a formulation-free strategy enlarges the margin between classes and thus improves the model flexibility. The introduced two constraints are imposed either exactly (on small data sets) or approximately (on large data sets) in our model, which provides a controllable trade-off between model flexibility and complexity with theoretical demonstration. In algorithm optimization, the objective function of our learning framework is proven to be gradient-Lipschitz continuous. Thereby, kernel and classifier/regressor learning can be efficiently optimized in a unified framework via Nesterov's acceleration. For the scalability issue, we study a decomposition-based approach to our model in the large sample case. The effectiveness of this approximation is illustrated by both empirical studies and theoretical guarantees. Experimental results on various classification and regression benchmark data sets demonstrate that our non-parametric kernel learning framework achieves good performance when compared with other representative kernel learning based algorithms.

关键词： support vector machines non-parametric kernel learning gradient-Lipschitz continuous

来源：评论

学校读者我要写书评

暂无评论

Author Correction: Discovering CRISPR-Cas system with self-processing pre-crRNA capability by foundation models

引用

Nature communications 2025年第1期16卷 535页

作者： Wenhui Li Xianyue Jiang Wuke Wang Liya Hou Runze Cai Yongqian Li Qiuxi Gu Qinchang Chen Peixiang Ma Jin Tang Menghao Guo Guohui Chuai Xingxu Huang Jun Zhang Qi Liu State Key Laboratory of Cardiology and Medical Innovation Center Shanghai East Hospital Frontier Science Center for Stem Cell Research Bioinformatics Department School of Life Sciences and Technology Tongji University Shanghai China. Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University) Ministry of Education Orthopaedic Department of Tongji Hospital Frontier Science Center for Stem Cell Research Bioinformatics Department School of Life Sciences and Technology Tongji University Shanghai China. Research Center for Life Sciences Computing Zhejiang Lab Hangzhou Zhejiang China. State Key Laboratory of Reproductive Medicine and Offspring Health Women's Hospital of Nanjing Medical University Nanjing Maternity and Child Health Care Hospital Nanjing Medical University Nanjing China. Shanghai Key Laboratory of Orthopedic Implants Department of Orthopedic Surgery Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine Shanghai China. State Key Laboratory of Cardiology and Medical Innovation Center Shanghai East Hospital Frontier Science Center for Stem Cell Research Bioinformatics Department School of Life Sciences and Technology Tongji University Shanghai China. 18alexanderm117@***. Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University) Ministry of Education Orthopaedic Department of Tongji Hospital Frontier Science Center for Stem Cell Research Bioinformatics Department School of Life Sciences and Technology Tongji University Shanghai China. 18alexanderm117@***. National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai Research Institute for Intelligent Autonomous Systems Shanghai China. 18alexanderm117@***. Research Center for Life Sciences Computing Zhejiang Lab Hangzhou Zhejiang China. xingxuhuang@zju.edu.cn. The Key Laboratory of Pancreatic Diseases of Zhejiang Province

来源：评论

学校读者我要写书评

暂无评论

Secure localization algorithms in wireless sensor networks: A review 2nd

Secure localization algorithms in wireless sensor networks: ...

引用

2nd International Conference on Computer, Communication and Computational Sciences, IC4S 2017

作者： Wang, Hua Wen, Yingyou Lu, Yao Zhao, Dazhe Ji, Chunlin School of Computer Science & Engineering Northeastern University Shenyang110819 China State Key Laboratory of Software Architecture Neusoft Corporation Shenyang110179 China Beijing Jiaotong University Beijing100044 China Key Laboratory of Medical Image Computing Ministry of Education Northeastern University Shenyang110819 China Kuang-Chi Institute of Advanced Technology Shenzhen518000 China

ISBN: (纸本)9789811303432

Localization plays a vital role in understanding the application context for wireless sensor networks (WSNs). However, it is vulnerable to various threats due to the open arrangement area, the nature of radio broadcasting, and resource constraint. Two kinds of attacks on localization process need to be investigated. On the one hand, adversaries may capture, impersonate, replicate, or fabricate nodes to mislead the target to an incorrect position. On the other hand, adversaries may tamper, interfere, replay, or modify jointly localization information to disturb the localization. This paper describes two kinds of localization attacks and gives a systematic survey of existing secure localization solutions, including the design ideas, application scopes, and limitations. Next, several novel secure localization schemes for special WSNs applications, including Underwater Wireless Sensor Networks (UWSNs), Wireless Body Area Network (WBAN) and three-dimensional (3D), are analyzed. Finally, many open problems for secure localization are presented. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Wireless sensor networks

来源：评论

学校读者我要写书评

暂无评论

Weakly supervised histopathology tissue semantic segmentation with multi-scale voting and online noise suppression

引用

Engineering Applications of Artificial Intelligence 2025年 156卷

作者： Xipeng Pan Hualong Zhang Huahu Deng Huadeng Wang Lingqiao Li Zhenbing Liu Lin Wang Yajun An Cheng Lu Zaiyi Liu Chu Han Rushi Lan School of Computer Science and Information Security Guilin University of Electronic Technology Guilin Guangxi 541004 China Department of Nasopharyngeal Carcinoma Sun Yat-sen University Cancer Center Guangzhou 510060 China Department of Radiology Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences) Southern Medical University Guangzhou 510080 Guangdong China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People’s Hospital Guangzhou 510080 Guangdong China International Joint Research Laboratory of Spatio-temporal Information and Intelligent Location Services Guilin University of Electronic Technology Guilin 541004 Guangxi China

The development of an Artificial Intelligence (AI) assisted tissue segmentation method of digital pathology images is critical for cancer diagnosis and prognosis. Excellent performance has been achieved with the current fully supervised segmentation approach, which relies on a huge number of annotated data. However, drawing dense pixel-level annotations on the giga-pixel whole slide image (WSI) is extremely time-consuming and labor-intensive. To this end, we propose a tissue segmentation method using only patch-level classification labels to reduce such annotation burden and significantly improve the quality of the pseudo-masks. We introduce a framework with two phases of classification and segmentation. In the classification phase, we propose a multi-scale voting method on the Class Activation Map (CAM) based model to obtain more stable pseudo masks. In the segmentation phase, an Online Noise Suppression Strategy (ONSS) is proposed to encourage the model to focus on more reliable signals in the pseudo mask rather than noisy signals. Extensive experiments on two weakly supervised pathology image tissue segmentation datasets Lung Adenocarcinoma (LUAD-HistoSeg) and Breast Cancer Semantic Segmentation (BCSS-WSSS) demonstrate our model outperforms state-of-the-art weakly-supervised semantic segmentation (WSSS) methods using patch-level labels. Furthermore, our method exhibits superior generalization ability compared to other models, and demonstrates promising adaptation performance on unseen domains with only small amounts of data.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Correction to: Enhancing cyber defense strategies with discrete multi-dimensional Z-numbers: a multi-attribute decision-making approach

引用

Complex & intelligent Systems 2025年第7期11卷 1-1页

作者： Yao, Aiting Huang, Chen Zhang, Weiqi Dong, Chengzu Lu, Meiqu Mao, Junjun Liu, Xiao Li, Xuejun Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging School of Computer Science and Technology Anhui University Hefei China AI Techplus Pty Ltd Organization Melbourne Australia School of Information Technology Deakin University Geelong Australia School of Data Science Lingnan University Hongkong China School of Artificial Intelligence Guangxi Minzu University Nanning China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei China

来源：评论

学校读者我要写书评

暂无评论

An ensemble framework with l_(21)-norm regularized hypergraph laplacian multi-label learning for clinical data prediction

An ensemble framework with l_(21)-norm regularized hypergrap...

引用

IEEE International Conference on Bioinformatics and Biomedicine

作者： Peng Cao Jinzhu Yang Shanshan Tang Dazhe Zhao Osmar Zaiane Min Huang Amine Trabelsi College of Computer Science and Engineering Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University College of Computer Science and Engineering Northeastern University College of Information Science and Engineering Northeastern University Department of Computing Science University of Alberta Department of Computing Science University of Alberta

ISBN: (纸本)9781728118680

Previous work has shown that machine learning algorithms lend themselves to clinical decision-making and are a valuable tool for physicians. For clinical data, it is often necessary to assign multiple labels to a patient record by choosing from a large number of potential labels. A key problem in learning from multi-labelled data is how to exploit the information contained in the correlations between labels. The hypergraph-based multi-label learning method learns from data by exploiting the spectral property of the hypergraph that encodes the correlation structure of labels. However, the problem with this method is the difficulty with which interpretations can be made. This is mainly due to its inability to recognize the importance of key features in the original feature space. Moreover, it is hard to comprehensively capture the complex structure of the correlations between labels. To overcome these difficulties and improve interpretability, we propose an l_(21)-norm regularized Graph Laplacian multi-label learning to perform feature selection and label embedding simultaneously. In-depth experimental studies, using the publicly available medical Information Mart for Intensive Care (MIMIC-III) database, validate the effectiveness of our approach.

关键词： Multi-label learning High dimensionality Feature selection Ensemble classification MIMIC-III Feature extraction hypergraph patient records Laplacian operator Learning spectral characteristic Machine learning algorithms interpretability Eigenvector

来源：评论

学校读者我要写书评

暂无评论

Feature-aware Multi-task feature learning for Predicting Cognitive Outcomes in Alzheimer's disease

Feature-aware Multi-task feature learning for Predicting Cog...

引用

IEEE International Conference on Bioinformatics and Biomedicine

ISBN: (纸本)9781728118680

Machine learning algorithms and multivariate data analysis methods have been widely utilized in the field of Alzheimer's disease (AD) research in recent years. Predicting cognitive performance of subjects from neuroimage measures and identifying relevant imaging biomarkers are important research topics in the study of Alzheimer's disease. Multi-task based feature learning (MTFL) have been widely studied to select a discriminative feature subset from MRI features, and improve the performance by incorporating inherent correlations among multiple clinical cognitive measures. It is known that the brain imaging measures are often correlated with each other, and AD is closely related to the inter-correlation among different brain regions. However, the multitask based feature learning (MTFL) method neglects the inherent correlation among brain imaging measures. We present a novel regularized multi-task learning approach via a joint sparsity-inducing regularization to effectively incorporate both a relatedness among multiple cognitive score prediction tasks and a useful inherent correlation between brain imaging measures by exploiting correlations among features. It allows the simultaneous selection of a common set of biomarkers for all tasks and the preservation of the inherent structure of imaging measures. The reported experiments on the ADNI dataset show that the proposed method is effective and promising.

关键词： Alzheimer's disease Regression model Multi-task learning Magnetic resonance imaging Biomarkers discovery Alzheimer Disease simple regression (model) Magnetic Resonance Imaging PARALLEL PROCESSING (COMPUTERS) Learning Machine learning algorithms Diencephalon

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：