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3rd International Conference on Innovation in Artificial Intelligence, ICIAI 2019

作者： Gao, Mengna Zhang, Qiang Dong, Jing Yang, Deyun Zhou, Dongsheng Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian116024 China School of Information Science and Technology Taishan University Tai'an Shandong Province271000 China Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian116024 China

ISBN: (纸本)9781450361286

Real-time speech emotion recognition has always been a problem. To this end, we proposed an end-to-end speech emotion recognition model based on one-dimensional convolutional neural network, which contains only three convolution layers, two pooling layers and one full-connected layer. Through Adam optimization algorithm and back propagation mechanism, more discriminative features can be extracted continuously. Our model is quite simple in structure and easy to quickly complete the emotional classification task. Compared with traditional methods, there is no need to carry out the complex process of manually extracting features, and the model can automatically learn the emotional features from raw speech signals. In the emotional recognition experiments with EMODB, CASIA, IEMOCAP, and CHEAVD four speech databases, relatively high recognition rates were obtained. Experiments show that the proposed algorithm is of great benefit to the implementation of real-time speech emotion recognition. © 2019 ACM

关键词： Emotion Recognition

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Modelling and analysis of haemoglobin catalytic reaction kinetic system

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Mathematical & Computer Modelling of Dynamical Systems 2020年第4期26卷 306-321页

作者： Liu, Yanhong Lv, Hui Wang, Bin Yang, Deyun Zhang, Qiang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Dalian University Dalian China Department of Information Science and Technology Taishan University Taian China

In order to study whether haemoglobin (Hb) can replace peroxidase and has good catalytic properties. The key to exploring the characteristics of Hb peroxidase is to establish a suitable kinetic model, which is studied in this paper. First, according to the Hb catalytic reaction, a nonlinear system is established and improved. It is proved that the established system is in line with the practical significance. The stability of the original system is judged by analysing the stability of the simplified system. Then, considering the effect of time delay on Hb catalytic reaction, a nonlinear time-delay catalytic reaction system is obtained. For convenient application, the system is linearized using Taylor's formula, and the dynamic characteristics of Hopf bifurcation are analysed. The response diagrams of three system are plotted by setting perturbation parameters, and their variations are observed to analyse the differences among them. The results show that the nonlinear time-delay system can better describe the characteristics of the catalytic reaction. [ABSTRACT FROM AUTHOR]

关键词： central manifold theorem haemoglobin Hopf bifurcation Nonlinear system model time delay

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Graph Confidence Intercalibration Network for Intracranial Aneurysm Lesion Instance Segmentation in Dsa

SSRN

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SSRN 2024年

作者： Ye, Haili Mo, Yancheng Tang, Chen Liao, Mingqian Zhang, Xiaoqing Dai, Limeng Li, Baihua Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Computer Science Loughborough University LoughboroughLE11 3TU United Kingdom School of Ophthalmology and Optometry Eye Hospital Wenzhou Medical University Wenzhou325027 China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen People’s Hospital Neurosurgery Department Shenzhen518020 China China

Intracranial aneurysm (IA) lesion segmentation is significant for its treatment and prognosis. Although exiting deep network-based instance methods have good IA lesion segmentation results based on digital subtraction angiography (DSA) images, they still face great challenges with instance confidence bias and imprecise boundary segmentation, which may negatively affect IA diagnosis. To tackle these problems, this paper proposes a novel graph confidence intercalibration network (GCINet) to automatically segment IA lesions from DSA images. To be specific, we design a graph confidence intercalibration (GCI) module to mitigate instance confidence bias by dynamically adjusting their confidence distributions. At the same time, we propose an edge space perception (ESP) module to correct ambiguous segmentation boundaries. Extensive experiments on a clinical IA-DSA and a publicly available LiTS dataset demonstrate that our GCINet outperforms state-of-the-art methods. Additionally, visual analysis and ablation studies are provided to verify the effectiveness of each module in GCINet. © 2024, The Authors. All rights reserved.

关键词： Image segmentation

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Addressing Fairness Issues in Deep Learning-Based Medical Image Analysis: A Systematic Review

arXiv

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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

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

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

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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

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

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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

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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

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Preface

Lecture Notes in Control and Information Sciences

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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

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Landmark Detection using Transformer Toward Robot-assisted Nasal Airway Intubation

arXiv

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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

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

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

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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

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