检索结果-内蒙古大学图书馆

arXiv 2022年

作者： Xu, Zikang Li, Jun Yao, Qingsong Li, Han Zhao, Mingyue Kevin Zhou, S. School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Anhui Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Jiangsu Suzhou215123 China Institute of Computing Technology CAS Beijing100190 China Key Laboratory of Precision and Intelligent Chemistry University of Science and Technology of China Anhui Hefei230026 China

Deep learning algorithms have demonstrated remarkable efficacy in various medical image analysis (MedIA) applications. However, recent research highlights a performance disparity in these algorithms when applied to specific subgroups, such as exhibiting poorer predictive performance in elderly females. Addressing this fairness issue has become a collaborative effort involving AI scientists and clinicians seeking to understand its origins and develop solutions for mitigation within MedIA. In this survey, we thoroughly examine the current advancements in addressing fairness issues in MedIA, focusing on methodological approaches. We introduce the basics of group fairness and subsequently categorize studies on fair MedIA into fairness evaluation and unfairness mitigation. Detailed methods employed in these studies are presented too. Our survey concludes with a discussion of existing challenges and opportunities in establishing a fair MedIA and healthcare system. By offering this comprehensive review, we aim to foster a shared understanding of fairness among AI researchers and clinicians, enhance the development of unfairness mitigation methods, and contribute to the creation of an equitable MedIA society. © 2022, CC BY.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Answer-Centric Local and Global Information Fusion for Conversational Question Generation

Answer-Centric Local and Global Information Fusion for Conve...

引用

IEEE International Conference on Big Knowledge (ICBK)

作者： Panpan Lei Xiao Sun Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei Anhui China

ISBN: (纸本)9781665438599

Conversational Question Generation (CQG) is a new concern in Question Generation (QG) study. Recently Seq2Seq neural network model has been widely used in the QG area. CQG model is also based on the Seq2Seq neural network model. We note a problem: the CQG model's input is not a single sentence, but a long text and conversation history. Seq2Seq model can't effectively process long input, the model will generate questions not related to the answer. To solve this problem, we propose an answer-centric local and global information fusion model. We extract the evidence sentence containing the answer in the passage and encode the evidence sentence and the passage information separately. On the one hand, we add answer-centered position tags in the passage to reinforce the attention of information related to the answer. On the other hand, we put the key sentence into the question type prediction model. By combining the answer position embedding to predict the question type, and then put the predicted question types in the key sentence to guide the generation of the question. Finally, we use a gate mechanism to merge key sentence information and passage information. The experimental results show that we have achieved better results.

关键词： Conferences Neural networks Predictive models Logic gates History Data mining

来源：评论

学校读者我要写书评

暂无评论

A Lightweight, Real-Time, and High-Precision Calibrated Photometric Stereo Measurement Method

引用

IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Zhao Zhang Han Feng Xiaoyan Tian Peiyan Hu Yuetong Chang Guangming Xia Shuxian Shang Biwei Wu Weibo Wang Center of Ultra-Precision Optoelectronic Instrument Engineering and the Key Laboratory of Ultra-Precision Intelligent Instrumentation Ministry of Industry and Information Technology Harbin Institute of Technology Harbin China National Key Laboratory of Advanced Vehicle Integration and Control China FAW Group Company Ltd. Changchun China Faculty of Computing Harbin Institute of Technology Harbin China

Photometric stereo (PS) represents a sophisticated technique utilized to capture high-fidelity surface textures and morphologies under varying illumination angles, with substantial applications in fields such as quality assurance and cultural heritage preservation. However, contemporary implementations often grapple with challenges in achieving high-precision measurements that are both economically feasible and conducive to real-time processing. To address these challenges, we propose a dual-scale feature fusion (DSFF) PS network coupled with a shadow-fusion supervised (SFS) training strategy. By aggregating dual-scale features and effectively leveraging shadow-occluded regions, the proposed approach overcomes the limitations of existing techniques, enabling low-cost, real-time, and high-precision measurement of complex morphologies across diverse materials. Experimental results on four authoritative public datasets demonstrate that our methods outperform nearly 30 state-of-the-art approaches in key metrics such as measurement accuracy, memory consumption, and inference time. These results highlight the breakthrough improvements and the immense potential for application in various fields. The code used in this study is publicly accessible at https://***/zz-ux/PS-Measurement

关键词： Surface morphology Accuracy Surface reconstruction Lighting Reflectivity Morphology Mathematical models Image reconstruction Training Feature extraction

来源：评论

学校读者我要写书评

暂无评论

A Novel Spatial-Temporal Convolutional Neural Network for Remote Photoplethysmography 12

A Novel Spatial-Temporal Convolutional Neural Network for Re...

引用

12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019

作者： Hu, Min Guo, Dong Wang, Xiaohua Ge, Peng Chu, Qian School of Computer and Information of Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei China

ISBN: (纸本)9781728148526

Remote photoplethysmography (rPPG) using a simple consumer-level webcam has great potential for healthcare, human-computer interaction, affective computing, and many other fields. However, traditional methods based on the skin reflection model and signal processing achieved limited success. Thus, this paper presents a novel end-to-end spatial-temporal convolutional neural network for the rPPG recovering task: To preserve the details, the pooling layer is moved to the branch network, and the attention mechanism is used to enhance the backbone network performance. Moreover, the ConvLSTM was used to learn the relationship between frames. The manual features extraction and signal post-processing steps can be omitted through our end-to-end neural network. The experimental results conducted on PURE and COHFACE datasets show that the proposed method significantly improved the quality of the recovered rPPG signal. © 2019 IEEE.

关键词： Photoplethysmography

来源：评论

学校读者我要写书评

暂无评论

Preface

Lecture Notes in Control and Information Sciences

引用

Lecture Notes in Control and Information Sciences 2023年 492卷 v-vi页

作者： Luan, Xiaoli He, Shuping Liu, Fei Key Laboratory of Advanced Process Control for Light Industry [Ministry of Education Institute of Automation Jiangnan University Jiangsu Wuxi China Key Laboratory of Intelligent Computing and Signal Processing [Ministry of Education School of Electrical Engineering and Automation Anhui University Anhui Hefei China

来源：评论

学校读者我要写书评

暂无评论

Landmark Detection using Transformer Toward Robot-assisted Nasal Airway Intubation

arXiv

引用

arXiv 2023年

作者： Liu, Tianhang Li, Hechen Bai, Long Wu, Yanan Wang, An Islam, Mobarakol Ren, Hongliang Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong College of Medicine and Biological Information Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China University College London London United Kingdom Department of Biomedical Engineering National University of Singapore Singapore Shun Hing Institute of Advanced Engineering The Chinese University of Hong Kong Hong Kong Shenzhen Research Institute The Chinese University of Hong Kong Shenzhen China

Robot-assisted airway intubation application needs high accuracy in locating targets and organs. Two vital landmarks, nostrils and glottis, can be detected during the intubation to accommodate the stages of nasal intubation. Automated landmark detection can provide accurate localization and quantitative evaluation. The Detection Transformer (DeTR) leads object detectors to a new paradigm with long-range dependence. However, current DeTR requires long iterations to converge, and does not perform well in detecting small objects. This paper proposes a transformer-based landmark detection solution with deformable DeTR and the semantic-aligned-matching module for detecting landmarks in robot-assisted intubation. The semantics aligner can effectively align the semantics of object queries and image features in the same embedding space using the most discriminative features. To evaluate the performance of our solution, we utilize a publicly accessible glottis dataset and automatically annotate a nostril detection dataset. The experimental results demonstrate our competitive performance in detection accuracy. Our code can be accessible at https://***/ConorLTH/airway intubation landmarks detection. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Prediction model of bone drilling temperature based on heat source method in surgical rehabilitation

引用

Procedia CIRP 2020年 89卷 263-269页

作者： Yahui Hu Zhiqiang Yan Xiangjun Li Chunqiu Zhang Qingchun Zheng Tianjin Key Laboratory of the Design and Intelligent Control of the Advanced Mechatronical System Tianjing University of Technology Tianjin China School of Mechanical Engineering Tianjin University of Technology Tianjin China

In order to feedback the cutting temperature in real time during the operation with the surgical robot, this study established a temperature prediction model to analyze the temperature distribution of the cortex. In this work, a three-dimensional governing equation for heat transfer of bone tissue during drilling is proposed, and the heat transfer of cortical bone is evaluated by a reverse heat transfer method. The prediction model was validated by experimental methods and the heat affected zone (HAZ) of cortical bone was analyzed. The results show that the prediction results are in good agreement with the relevant experimental results. HAZ increases with increasing rate of penetration; the closer the bone hole is, the earlier the temperature rises; and the temperature at which the cortical bone is difficult to measure (radial distance x < 0.5 mm) is predicted. The predictive model can accurately predict the temperature distribution of the cortical bone and prevent the occurrence of thermal damage. Provide technical support for the development of medical rehabilitation robots. And reduce the patient’s secondary injury, which is conducive to rapid recovery after surgery.

关键词： Cortical bone drilling Temperature estimation Inverse heat transfer problem Thermal damage rehabilitative

来源：评论

学校读者我要写书评

暂无评论

Thermal network model and experimental validation for a motorized spindle including thermal–mechanical coupling effect

Research Square

引用

Research Square 2021年

作者： Zhou, Changjiang Qu, Zefeng Hu, Bo Li, Shengbo State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China Hunan Provincial Key Laboratory of Intelligent Manufacturing Technology for High-performance Mechanical Equipment Changsha University of Science and Technology Changsha410114 China College of Mechanical and Vehicle Engineering Xiamen University of Technology Xiamen361024 China

Thermal deformation caused by temperature rise have an influence on the dynamic performance of a motorized spindle. In turn, the change in the dynamic performance will affect the temperature rise and thermal deformation of the system. However, the latter was rarely focused on in the previous literature. Therefore, a thermal network model of motorized spindle is enhanced by considering the thermal–mechanical coupling effect. Then, an iterative method is presented to solve the coupled equations, and a temperature test rig of the motorized spindle is set up to verify the proposed model. The relative error between the predicted and experimental results at two test points decreases by 9.56% and 3.44% after considering the thermal–mechanical coupling effect. The comparison with the experimental results shows that the proposed model with thermal–mechanical coupling effect can obtain a more accurate temperature field than the previous model. © 2021, CC BY.

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

A safety-reinforced mutual pulse heating strategy based on microscopic-state estimation for power-redistributable lithium-ion battery pack

引用

Energy 2025年 330卷

作者： Hao Zhong Fei Lei Jie Liu Fei Ding Wenhao Zhu Haijun Wang School of Intelligent Engineering and Intelligent Manufacturing Hunan University of Technology and Business Changsha 410205 PR China State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha 410082 PR China College of Automotive and Mechanical Engineering Changsha University of Science and Technology Changsha 410114 PR China

advanced heating is essential for mitigating temporary losses in battery capacity and peak power at low temperatures, thereby enhancing utilization efficiency and reducing safety risks. Expanding upon our prior research, this paper proposes a safety-reinforced mutual pulse heating (MPH) strategy based on microscopic-state estimation, in which multiplexing converter-based drivers are reused as on-board heating topologies. An adaptive super-twisting sliding mode observer is utilized to capture battery solid-liquid phase Li + concentration, electrode and electrolyte potentials, and side-reaction overpotentials. Then, a microscopic-state constraints-based MPH current prediction method is formulated to delineate the safe operational area of battery micro-states. The MPH current amplitude is updated by an online negative feedback-based optimization controller, while the heating duty ratio is still adjusted using fuzzy feedback mechanisms. Experimental validation of the proposed method is conducted across five critical metrics: heating duration, temperature-rise rate, safety risk, temperature uniformity, and degradation. The method demonstrated its efficacy by elevating the battery temperature from −20 °C to 0 °C within 178.3 s, while keeping the microscopic states within safe thresholds, with a peak temperature gradient of <2 °C. During 800 heating cycles, battery integrity remains intact as evidenced by stable capacity, coulomb efficiency, internal resistance, and differential voltage curves.

关键词： Electrochemical-thermal model Lithium-ion battery Microscopic-state estimation Multiplexing converter Mutual pulse heating Safety enhancement

来源：评论

学校读者我要写书评

暂无评论

An Automatic Method for Brain Tumors Segmentation Based on Deep Convolutional Neural Network

An Automatic Method for Brain Tumors Segmentation Based on D...

引用

IEEE International Conference on Medical Imaging Physics and Engineering

作者： Hui Lin Benjian Zhang Xin Guo Dong Guo Jia Jing Luo Huiyuan Zhao Yumeng Min Hu Fuji Ren School of Physics Hefei University of Technology Hefei China Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei China School of Computer and Information Hefei University of Technology Hefei China Graduate School of Advanced Technology and Science University of Tokushima Tokushima Japan

ISBN: (纸本)9781665426091

[Purpose] Accurate outline of tumor targets is critical to a high quality radiotherapy plan. Manual segmentation is of great workload and has a strong artificial subjectivity. Using deep learning method to assist automatically segmenting of tumor target is the penetration and application of artificial intelligence in medicine. [Methods] A 6-layer model of deep Convolution Neural Network (CNN) has been constructed by taking advantage of different types of layers for brain tumor segmentation. This model is a 6 layer CNN model (6-CNN) composed of three convolution layers, two pool layers and one full connection layer. To obtain enough samples for 6-CNN model training, a patch-based technology has been adopted. That is to successively extract a local area from the whole image as a patch. And the center pixel value is taken as the pixel value of the whole patch. Similarly, the label of the center pixel is also taken as the label of the whole patch. Thus the 6-CNN model transforms the brain tumor image segmentation into patch classification based on the excellent classification characteristics of deep convolution neural network. The model combines the local features of patch, the information extracted from shallow network and the global features to predict the category label of the central pixel of patch. [Results] The model is validated on BRATS 2015 dataset and results show that the segmentation accuracy can be up to Dice Similarity Coefficient (DSC) $90\%\pm 4\%$ . [Conclusions] An automatic deep CNN segmentation model for brain tumors has been constructed based on MRI image patches, which is expected to assist or even substitute the manual segmentation of brain tumors.

关键词： Image segmentation Convolution Magnetic resonance imaging Transforms Predictive models Brain modeling Feature extraction

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：